#Modificar variable para especificar directorio del Proyecto Final
#local.path <- "/Users/akcasill/Downloads"

user.path <- "/Users/jos/Downloads/"

local.path <- paste(user.path ,"mcc-ad/data",sep = "")
local.path.imgs <- paste(user.path ,"mcc-ad/imgs",sep = "")
#Dependencies
#install.packages("png")
library(png)

#ASISTENCIAS TOTALES

setwd(local.path)
The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
# son 9 semestres de 6 materias cada uno. 
# 1.- Asistencias Totales
load("AsistenciasTotales.R")
class(asistencias.totales)
[1] "list"
length(asistencias.totales)
[1] 1000
class(asistencias.totales[[1]])
[1] "matrix"
dim(asistencias.totales[[1]])
[1] 32 54
class(asistencias.totales[1])
[1] "list"
asistencias.totales[[1]][1:10,1:10]
      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
 [1,]    2    2    2    2    2    2    1    2    2     2
 [2,]    2    2    2    2    2    0    2    2    2     2
 [3,]    2    2    2    2    2    2    2    0    2     2
 [4,]    2    2    2    2    2    2    2    0    2     2
 [5,]    2    1    2    1    2    1    2    2    2     2
 [6,]    2    1    2    2    2    0    0    0    2     2
 [7,]    1    2    2    1    2    0    2    2    2     2
 [8,]    2    2    2    0    2    1    1    1    2     2
 [9,]    2    2    2    0    1    2    2    0    2     2
[10,]    2    2    2    2    2    2    2    0    0     2
#Asistencias
#===================
#Definición Valores
#===================
# 2 El alumno tiene asistnecia completa.
# 1 El alumno tiene retardo.
# 0 El alumno tiene falta.

#Sólo tomar las primeras 12 materias (Columnas)
for(i in 1:length(asistencias.totales)){
  asistencias.totales[[i]] <- asistencias.totales[[i]][,1:12]
}
asistencias.totales[[4]]
      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
 [1,]    2    2    2    2    2    2    0    2    2     2     2     2
 [2,]    2    2    2    2    2    1    2    2    2     2     2     2
 [3,]    2    2    2    2    0    2    2    1    2     2     2     1
 [4,]    2    2    2    2    2    2    2    2    2     2     2     2
 [5,]    2    1    2    2    2    2    2    2    2     2     2     2
 [6,]    2    1    2    2    1    1    2    1    2     2     1     2
 [7,]    1    2    2    2    2    1    2    2    2     2     2     2
 [8,]    2    2    2    2    1    2    1    0    2     2     2     2
 [9,]    2    2    2    1    0    2    2    1    0     2     2     2
[10,]    2    2    2    2    2    2    2    2    1     2     2     2
[11,]    2    2    2    2    2    2    2    2    2     2     2     2
[12,]    2    2    2    2    2    2    2    2    2     2     2     2
[13,]    2    2    2    2    2    2    2    2    1     2     2     2
[14,]    2    2    2    1    2    2    2    2    2     2     2     2
[15,]    2    2    2    2    2    2    2    2    2     2     2     2
[16,]    2    2    2    2    2    2    2    0    2     2     2     2
[17,]    2    1    2    1    2    2    2    2    2     2     2     2
[18,]    0    2    2    2    1    2    2    2    1     2     2     2
[19,]    2    2    2    1    2    2    0    2    2     2     2     2
[20,]    2    2    2    2    1    2    2    2    2     2     2     2
[21,]    1    2    2    2    1    1    2    2    1     2     2     2
[22,]    2    2    2    0    1    2    2    2    2     2     2     2
[23,]    2    2    2    2    2    2    0    2    2     2     2     2
[24,]    2    2    2    2    2    2    2    2    2     2     2     2
[25,]    2    2    2    2    2    2    0    2    2     2     2     2
[26,]    2    2    2    2    2    2    2    0    1     2     2     2
[27,]    2    2    2    2    2    2    2    2    2     2     2     2
[28,]    2    2    2    2    0    2    2    2    2     2     2     2
[29,]    2    1    2    2    2    2    1    2    1     2     2     2
[30,]    2    2    2    2    0    2    2    1    0     2     2     2
[31,]    2    2    2    2    2    2    2    2    2     2     2     2
[32,]    2    2    2    2    2    2    2    2    1     2     2     2

Tamaño de Lista de Asistencia de Alumnos:

length(asistencias.totales)
[1] 1000

Lista de total Asistencias por Alumno

#
#for(i in 1:length(asistencias.totales)){
#  asistencias.totales[[i]] <- asistencias.totales[[i]][,1:12]
#}
asistencias.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(asistencias.totales)){
  for(j in 1:12){
    asistencias.alumnos[i,j] <- sum(asistencias.totales[[i]][,j])/32    
  }
}

asistencias.alumnos[1,]
 [1] 1.87500 1.87500 2.00000 1.40625 1.90625 1.43750 1.59375 1.12500 1.84375 2.00000 1.96875 1.96875
asistencias.df <- as.data.frame(asistencias.alumnos)

#Asistencia Materias Ejemplo: AM1 = Asistencia Materia 1
colnames(asistencias.df) <- c('AM1','AM2','AM3','AM4','AM5','AM6','AM7','AM8','AM9','AM10','AM11','AM12')
#DATA FRAME DE ASISTENCIAS ALUMNOS
#=================================
#Suma de asistencias por Materia
#=================================
asistencias.df

#PERFIL ALUMNOS

setwd(local.path)
The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
print("Summary")
[1] "Summary"
load("perfilAlumnos.R")
#head(perfil.alumnos,1)
str(perfil.alumnos)
'data.frame':   1000 obs. of  7 variables:
 $ genero                  : int  2 2 2 1 2 2 2 2 1 2 ...
 $ admision.letras         : num  60.1 59.1 53.1 57 61.5 ...
 $ admision.numeros        : num  35.2 33.2 21.3 29 37.9 ...
 $ promedio.preparatoria   : num  70.3 67.2 60 61 74.4 ...
 $ edad.ingreso            : num  18 17 15 16 18 18 15 17 14 17 ...
 $ evalucion.socioeconomica: int  4 4 4 4 4 4 4 4 4 4 ...
 $ nota.conducta           : num  16 15 13 14 16 16 13 15 12 15 ...
summary(perfil.alumnos)
     genero      admision.letras admision.numeros promedio.preparatoria  edad.ingreso   evalucion.socioeconomica nota.conducta  
 Min.   :1.000   Min.   :44.94   Min.   : 4.878   Min.   : 60.00        Min.   :11.00   Min.   :1.000            Min.   : 9.00  
 1st Qu.:1.000   1st Qu.:56.61   1st Qu.:28.226   1st Qu.: 60.00        1st Qu.:16.00   1st Qu.:3.000            1st Qu.:14.00  
 Median :2.000   Median :59.98   Median :34.970   Median : 69.95        Median :17.00   Median :4.000            Median :15.00  
 Mean   :1.595   Mean   :60.06   Mean   :35.114   Mean   : 72.25        Mean   :17.53   Mean   :3.466            Mean   :15.53  
 3rd Qu.:2.000   3rd Qu.:63.64   3rd Qu.:42.275   3rd Qu.: 80.91        3rd Qu.:19.00   3rd Qu.:4.000            3rd Qu.:17.00  
 Max.   :2.000   Max.   :77.71   Max.   :70.411   Max.   :100.00        Max.   :25.00   Max.   :4.000            Max.   :20.00  
#===================
#Definición Valores
#===================
# Genero: 2 Hombre, 1 Mujer.
# admision.letras: Calificación Examen Admisión Español
# admision.numeros: Calificación Examen Admisión Matemáticas
# promedio.preparatoria: Calificación Promedio Preparatoria   
# edad.ingreso: Edad, variable numérica             
# evalucion.socioeconomica: 1 más privilegiado, 4 menos privilagiado
# nota.conducta: Calificación subjetiva. 
perfil.alumnos$genero <- factor(perfil.alumnos$genero)
perfil.alumnos$evalucion.socioeconomica <-
  factor(perfil.alumnos$evalucion.socioeconomica)

perfil.alumnos$edad.ingreso <- 
  factor(perfil.alumnos$edad.ingreso)

#DATAFRAME CALIFICACIONES ALUMNOS

setwd(local.path)
The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
print("Summary")
[1] "Summary"
# 3 1000 matrices de 2 x 54, calificación entre 1 y 20
load("ResultadosExamenes.R")
#resultados.examenes.totales

examenes.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(resultados.examenes.totales)){
  for(j in 1:12){
    examenes.alumnos[i,j] <- sum(resultados.examenes.totales[[i]][,j])/2    
  }
}

examenes.alumnos[1,]
 [1] 11.956449 12.330884 12.463337 15.189492 12.328150 17.087821  9.466637 12.011178 11.368753 12.221370 11.416652 12.330704
cal.alumnos.df <- as.data.frame(examenes.alumnos)
#Calificaciones Materias Ejemplo: CM2 = Calificiación Promedio Materia 2
colnames(cal.alumnos.df) <- c('CM1','CM2','CM3','CM4','CM5','CM6','CM7','CM8','CM9','CM10','CM11','CM12')
#===================
#Definición Valores
#===================
# CM1: Calificación Materia 1 valor Máximo 20
cal.alumnos.df

#TRABAJOS POR CLASE

setwd(local.path)
The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
print("Summary")
[1] "Summary"
# 4 1000 matrices de 4 x 54, son 4 trabajos por clase, entre 1 y 20
load("ResultadoTrabajos.R")
resultados.trabajos.totales[[2]][,1]
[1] 11.79653 12.11637 12.71856 13.72462
tareas.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(resultados.trabajos.totales)){
  for(j in 1:12){
    tareas.alumnos[i,j] <- sum(resultados.trabajos.totales[[i]][,j])/4    
  }
}

#tareas.alumnos[1,]
tareas.alumnos.df <- as.data.frame(tareas.alumnos)
#Tareas Materias Ejemplo: TM2 = Tareas Promedio Materia 2
colnames(tareas.alumnos.df) <- c('TM1','TM2','TM3','TM4','TM5','TM6','TM7','TM8','TM9','TM10','TM11','TM12')
#===================
#Definición Valores
#===================
# TM1: Calificación Tarea Materia 1 valor Máximo 20
tareas.alumnos.df

#VISITAS BIBLIOTECA

# 5 Redondear. Uso físico y virtual. vector. 1000 Matrices, número de veces que asistio a la biblioteca por materia
setwd(local.path)
The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
load("UsoBiblioteca.R")
length(uso.biblioteca.totales)
[1] 1000
mi.val <- uso.biblioteca.totales[[1]][1,1]
mi.val
[1] 12.65509
mi.val <- as.data.frame(mi.val)
mi.val

visitas.biblio.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    visitas.biblio.alumnos[i,j] <- uso.biblioteca.totales[[i]][1,j]
  }
}

visitas.biblio.alumnos.df <- as.data.frame(visitas.biblio.alumnos)
#Visitas Biblioteca  Ejemplo: VBM2 = Visitas Biblioteca  Materia 2
colnames(visitas.biblio.alumnos.df) <- c('VBM1','VBM2','VBM3','VBM4','VBM5','VBM6','VBM7','VBM8','VBM9','VBM10','VBM11','VBM12')
#===================
#Definición Valores
#===================
# VBM1: Visitas Biblioteca Materia 1
visitas.biblio.alumnos.df
NA
NA

#USO DE PLATAFORMAS DIGITALES

# 6 Redondear, vector. Uso de Canvas o de Plataforma digital.
setwd(local.path)
The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
load("UsoPlataforma.R")
#uso.plataforma.totales
uso.plataforma.totales[[1]][,1:12]
 [1] 32.796526 32.554647 32.504125 79.290015 32.600643 80.313415  5.944546 33.398886 32.664804 33.522435 32.831749 32.208083
plataformas.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    plataformas.alumnos[i,j] <- uso.plataforma.totales[[i]][1,j]
  }
}

#tareas.alumnos[1,]
plataformas.alumnos.df <- as.data.frame(plataformas.alumnos)
#Tareas Materias Ejemplo: TM2 = Tareas Promedio Materia 2
colnames(plataformas.alumnos.df) <- c('PDM1','PDM2','PDM3','PDM4','PDM5','PDM6','PDM7','PDM8','PDM9','PDM10','PDM11','PDM12')
#===================
#Definición Valores
#===================
# PDM1: Plataformas Digitales Materia 1 valor Máximo 20
plataformas.alumnos.df
NA

#APARTADO DE LIBROS POR MATERIA

# 7
setwd(local.path)
The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
load("ApartadoDeLibros.R") #1000 matrices, cantidad de libros que el alumno reservó por materia.
separacion.libros.totales[[1]][,1:12]
 [1] 1 1 1 3 1 3 0 1 1 1 1 1
reserva.libros.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    reserva.libros.alumnos[i,j] <- separacion.libros.totales[[i]][1,j]
  }
}

reserva.libros.alumnos.df <- as.data.frame(reserva.libros.alumnos)
#Reserva de Libris Ejemplo: RLM2 = Reserva de Libros Por Materia 2
colnames(reserva.libros.alumnos.df) <- c('RLM1','RLM2','RLM3','RLM4','RLM5','RLM6','RLM7','RLM8','RLM9','RLM10','RLM11','RLM12')
#===================
#Definición Valores
#===================
# RLM1: Reserva de Libros pro Materia 1
reserva.libros.alumnos.df
NA

#DISTRIBUCIÓN DE BECAS ALUMNOS

# 8 vector binario, 1 tiene beca, 0 no tiene Beca
setwd(local.path)
The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
load("Becas.R")
distribucion.becas[[1]]
[1] 0
sum(distribucion.becas)
[1] 163
becas.alumnos <- matrix(1:1000, nrow=1000, ncol=1)

for(i in 1:1000){
    becas.alumnos[i] <- distribucion.becas[i]
}

becas.alumnos.df <- as.data.frame(becas.alumnos)
colnames(becas.alumnos.df) <- c('BECA')
#===================
#Definición Valores
#===================
# BECA: Tiene Beca 1
becas.alumnos.df
#Necesita ser un factor por que solo tiene dos valores 0 o 1 
becas.alumnos.df$BECA <- as.factor(becas.alumnos.df$BECA)
becas.alumnos.df

#HISTORIAL DE PAGOS ALUMNOS

# 9  2 en tiempo, 1 retraso, 0, Son 9 semestres pero hay que user sólo 2 primeras columnas, 4 pagos.
setwd(local.path)
The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
load("HistorialPagos.R")
length(registro.pagos)
[1] 1000
registro.pagos[[500]]
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,]    2    2    2    2    2    2    1    2    2
[2,]    2    1    1    2    2    2    2    2    2
[3,]    2    2    2    2    1    2    2    1    2
[4,]    1    2    2    2    2    2    2    1    2
pagos.alumnos <- matrix(1:2000, nrow=1000, ncol=2)

for(i in 1:1000){
  for(j in 1:2){
    pagos.alumnos[i,j] <- sum(registro.pagos[[i]][,j])/4    
  }
}

#tareas.alumnos[1,]
pagos.alumnos.df <- as.data.frame(pagos.alumnos)
#Pago Semestre: PSEM2 = Pago Semestre 2
colnames(pagos.alumnos.df) <- c('PSEM1','PSEM2')
#===================
#Definición Valores
#===================
# PSEM1: Suma de pagos semestre 1, 2 valor máximo.
pagos.alumnos.df
NA
datos.alumnos.df <- cbind.data.frame(perfil.alumnos,
                                     becas.alumnos.df,
                              asistencias.df,
                              cal.alumnos.df,
                              tareas.alumnos.df,
                              visitas.biblio.alumnos.df,
                              plataformas.alumnos.df,
                              reserva.libros.alumnos.df,
                              pagos.alumnos.df)
datos.alumnos.df
NA
str(datos.alumnos.df)
'data.frame':   1000 obs. of  82 variables:
 $ genero                  : Factor w/ 2 levels "1","2": 2 2 2 1 2 2 2 2 1 2 ...
 $ admision.letras         : num  60.1 59.1 53.1 57 61.5 ...
 $ admision.numeros        : num  35.2 33.2 21.3 29 37.9 ...
 $ promedio.preparatoria   : num  70.3 67.2 60 61 74.4 ...
 $ edad.ingreso            : Factor w/ 15 levels "11","12","13",..: 8 7 5 6 8 8 5 7 4 7 ...
 $ evalucion.socioeconomica: Factor w/ 4 levels "1","2","3","4": 4 4 4 4 4 4 4 4 4 4 ...
 $ nota.conducta           : num  16 15 13 14 16 16 13 15 12 15 ...
 $ BECA                    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ AM1                     : num  1.88 1.88 1.88 1.88 1.88 ...
 $ AM2                     : num  1.88 1.41 1.41 1.88 1.41 ...
 $ AM3                     : num  2 2 2 2 1.72 ...
 $ AM4                     : num  1.41 1.81 1.81 1.81 1.81 ...
 $ AM5                     : num  1.91 1.5 1.38 1.56 1.91 ...
 $ AM6                     : num  1.44 1.38 1.12 1.88 1.12 ...
 $ AM7                     : num  1.59 1.69 1.69 1.69 1.69 ...
 $ AM8                     : num  1.12 1.12 1.94 1.69 1.12 ...
 $ AM9                     : num  1.84 1.84 1.84 1.66 1.66 ...
 $ AM10                    : num  2 2 1.22 2 2 ...
 $ AM11                    : num  1.97 1.97 1.97 1.97 1.97 ...
 $ AM12                    : num  1.97 1.97 1.97 1.97 1.97 ...
 $ CM1                     : num  12 12 12 12 12 ...
 $ CM2                     : num  12.3 12.3 15.8 15.8 12.3 ...
 $ CM3                     : num  12.5 12.5 12.5 16 16 ...
 $ CM4                     : num  15.2 11.9 11.9 15.2 11.9 ...
 $ CM5                     : num  12.3 15.8 12.3 12.3 12.3 ...
 $ CM6                     : num  17.1 17.1 13.3 17.1 13.3 ...
 $ CM7                     : num  9.47 13.08 13.08 13.08 13.08 ...
 $ CM8                     : num  12.01 15.35 12.01 7.69 15.35 ...
 $ CM9                     : num  11.37 11.37 11.37 18.25 6.61 ...
 $ CM10                    : num  12.2 12.2 12.2 15.6 12.2 ...
 $ CM11                    : num  11.4 11.4 11.4 11.4 11.4 ...
 $ CM12                    : num  12.3 12.3 12.3 15.8 12.3 ...
 $ TM1                     : num  12.6 12.6 12.6 12.6 12.6 ...
 $ TM2                     : num  12.2 12.2 15.6 15.6 12.2 ...
 $ TM3                     : num  12.3 12.3 12.3 15.7 15.7 ...
 $ TM4                     : num  15.2 11.9 11.9 15.2 11.9 ...
 $ TM5                     : num  12.6 16.1 12.6 12.6 12.6 ...
 $ TM6                     : num  16.2 16.2 12.6 16.2 12.6 ...
 $ TM7                     : num  7.97 12.18 12.18 12.18 12.18 ...
 $ TM8                     : num  12.59 16.13 12.59 8.66 16.13 ...
 $ TM9                     : num  11.5 11.5 11.5 18.33 6.84 ...
 $ TM10                    : num  12.5 12.5 12.5 15.9 12.5 ...
 $ TM11                    : num  11.6 11.6 11.6 11.6 11.6 ...
 $ TM12                    : num  12.6 12.6 12.6 16.1 12.6 ...
 $ VBM1                    : num  12.7 12.7 12.7 12.7 12.7 ...
 $ VBM2                    : num  11.8 11.8 27.8 27.8 11.8 ...
 $ VBM3                    : num  11.7 11.7 11.7 27.5 27.5 ...
 $ VBM4                    : num  33.8 15.9 15.9 33.8 15.9 ...
 $ VBM5                    : num  12 28 12 12 12 ...
 $ VBM6                    : num  34.1 34.1 16.1 34.1 16.1 ...
 $ VBM7                    : num  2.98 19.89 19.89 19.89 19.89 ...
 $ VBM8                    : num  14.66 31.99 14.66 1.93 31.99 ...
 $ VBM9                    : num  12.22 12.22 12.22 62.22 1.44 ...
 $ VBM10                   : num  15.1 15.1 15.1 32.6 15.1 ...
 $ VBM11                   : num  12.8 12.8 12.8 12.8 12.8 ...
 $ VBM12                   : num  10.7 10.7 10.7 26 10.7 ...
 $ PDM1                    : num  32.8 32.8 32.8 32.8 32.8 ...
 $ PDM2                    : num  32.6 32.6 59.2 59.2 32.6 ...
 $ PDM3                    : num  32.5 32.5 32.5 58.4 58.4 ...
 $ PDM4                    : num  79.3 33.8 33.8 79.3 33.8 ...
 $ PDM5                    : num  32.6 60 32.6 32.6 32.6 ...
 $ PDM6                    : num  80.3 80.3 33.8 80.3 33.8 ...
 $ PDM7                    : num  5.94 34.97 34.97 34.97 34.97 ...
 $ PDM8                    : num  33.4 73.31 33.4 3.33 73.31 ...
 $ PDM9                    : num  32.66 32.66 32.66 161.08 2.11 ...
 $ PDM10                   : num  33.5 33.5 33.5 75.4 33.5 ...
 $ PDM11                   : num  32.8 32.8 32.8 32.8 32.8 ...
 $ PDM12                   : num  32.2 32.2 32.2 53.5 32.2 ...
 $ RLM1                    : num  1 1 1 1 1 1 4 2 1 1 ...
 $ RLM2                    : num  1 1 2 2 1 0 2 1 1 2 ...
 $ RLM3                    : num  1 1 1 2 2 2 2 2 1 2 ...
 $ RLM4                    : num  3 1 1 3 1 3 3 3 1 1 ...
 $ RLM5                    : num  1 2 1 1 1 1 2 1 1 2 ...
 $ RLM6                    : num  3 3 1 3 1 5 0 3 1 3 ...
 $ RLM7                    : num  0 1 1 1 1 5 3 1 1 3 ...
 $ RLM8                    : num  1 2 1 0 2 1 4 0 1 2 ...
 $ RLM9                    : num  1 1 1 4 0 1 2 2 1 1 ...
 $ RLM10                   : num  1 1 1 3 1 3 3 3 5 3 ...
 $ RLM11                   : num  1 1 1 1 1 1 2 1 1 1 ...
 $ RLM12                   : num  1 1 1 2 1 0 2 1 1 1 ...
 $ PSEM1                   : num  2 2 2 2 2 2 1.5 2 2 2 ...
 $ PSEM2                   : num  2 1.5 1.5 2 2 1.5 2 2 2 2 ...
summary(datos.alumnos.df)
 genero  admision.letras admision.numeros promedio.preparatoria  edad.ingreso evalucion.socioeconomica nota.conducta   BECA   
 1:405   Min.   :44.94   Min.   : 4.878   Min.   : 60.00        17     :200   1: 56                    Min.   : 9.00   0:837  
 2:595   1st Qu.:56.61   1st Qu.:28.226   1st Qu.: 60.00        18     :167   2:107                    1st Qu.:14.00   1:163  
         Median :59.98   Median :34.970   Median : 69.95        19     :166   3:152                    Median :15.00          
         Mean   :60.06   Mean   :35.114   Mean   : 72.25        16     :140   4:685                    Mean   :15.53          
         3rd Qu.:63.64   3rd Qu.:42.275   3rd Qu.: 80.91        20     :113                            3rd Qu.:17.00          
         Max.   :77.71   Max.   :70.411   Max.   :100.00        15     : 98                            Max.   :20.00          
                                                                (Other):116                                                   
      AM1             AM2             AM3             AM4             AM5             AM6             AM7             AM8       
 Min.   :1.875   Min.   :1.406   Min.   :1.031   Min.   :1.031   Min.   :1.375   Min.   :1.125   Min.   :1.406   Min.   :1.125  
 1st Qu.:1.875   1st Qu.:1.875   1st Qu.:1.719   1st Qu.:1.406   1st Qu.:1.562   1st Qu.:1.438   1st Qu.:1.594   1st Qu.:1.875  
 Median :1.875   Median :1.875   Median :2.000   Median :1.812   Median :1.906   Median :1.875   Median :1.688   Median :1.938  
 Mean   :1.875   Mean   :1.779   Mean   :1.837   Mean   :1.658   Mean   :1.794   Mean   :1.720   Mean   :1.632   Mean   :1.801  
 3rd Qu.:1.875   3rd Qu.:1.875   3rd Qu.:2.000   3rd Qu.:1.812   3rd Qu.:1.906   3rd Qu.:1.875   3rd Qu.:1.688   3rd Qu.:1.938  
 Max.   :1.875   Max.   :1.875   Max.   :2.000   Max.   :1.812   Max.   :1.906   Max.   :1.875   Max.   :1.688   Max.   :1.938  
                                                                                                                                
      AM9             AM10            AM11            AM12            CM1              CM2              CM3              CM4        
 Min.   :1.562   Min.   :1.219   Min.   :1.562   Min.   :1.438   Min.   : 7.594   Min.   : 8.218   Min.   : 8.439   Min.   : 7.487  
 1st Qu.:1.797   1st Qu.:2.000   1st Qu.:1.969   1st Qu.:1.969   1st Qu.:11.956   1st Qu.:12.331   1st Qu.:12.463   1st Qu.:11.892  
 Median :1.844   Median :2.000   Median :1.969   Median :1.969   Median :11.956   Median :12.331   Median :12.463   Median :11.892  
 Mean   :1.781   Mean   :1.872   Mean   :1.892   Mean   :1.875   Mean   :12.751   Mean   :13.567   Mean   :13.701   Mean   :13.064  
 3rd Qu.:1.844   3rd Qu.:2.000   3rd Qu.:1.969   3rd Qu.:1.969   3rd Qu.:11.956   3rd Qu.:15.775   3rd Qu.:15.951   3rd Qu.:15.189  
 Max.   :1.844   Max.   :2.000   Max.   :1.969   Max.   :1.969   Max.   :18.638   Max.   :18.887   Max.   :18.976   Max.   :18.595  
                                                                                                                                    
      CM5              CM6             CM7              CM8              CM9              CM10             CM11             CM12       
 Min.   : 8.214   Min.   : 9.86   Min.   : 9.467   Min.   : 7.685   Min.   : 6.615   Min.   : 8.036   Min.   : 6.694   Min.   : 8.218  
 1st Qu.:12.328   1st Qu.:13.32   1st Qu.:13.080   1st Qu.:12.011   1st Qu.:11.369   1st Qu.:12.221   1st Qu.:11.417   1st Qu.:12.331  
 Median :12.328   Median :13.32   Median :13.080   Median :12.011   Median :11.369   Median :12.221   Median :11.417   Median :12.331  
 Mean   :13.582   Mean   :14.72   Mean   :14.600   Mean   :13.233   Mean   :12.493   Mean   :13.488   Mean   :12.466   Mean   :13.539  
 3rd Qu.:15.771   3rd Qu.:17.09   3rd Qu.:16.773   3rd Qu.:15.348   3rd Qu.:14.492   3rd Qu.:15.628   3rd Qu.:14.556   3rd Qu.:15.774  
 Max.   :18.885   Max.   :19.54   Max.   :19.387   Max.   :18.674   Max.   :18.246   Max.   :18.814   Max.   :18.278   Max.   :18.887  
                                                                                                                                       
      TM1              TM2              TM3             TM4              TM5              TM6              TM7              TM8        
 Min.   : 8.648   Min.   : 8.036   Min.   : 8.11   Min.   : 7.457   Min.   : 8.608   Min.   : 8.735   Min.   : 7.965   Min.   : 8.657  
 1st Qu.:12.589   1st Qu.:12.221   1st Qu.:12.27   1st Qu.:11.874   1st Qu.:12.565   1st Qu.:12.641   1st Qu.:12.179   1st Qu.:12.594  
 Median :12.589   Median :12.221   Median :12.27   Median :11.874   Median :12.565   Median :12.641   Median :12.179   Median :12.594  
 Mean   :13.375   Mean   :13.437   Mean   :13.47   Mean   :13.043   Mean   :13.865   Mean   :13.931   Mean   :13.539   Mean   :13.920  
 3rd Qu.:12.589   3rd Qu.:15.629   3rd Qu.:15.69   3rd Qu.:15.166   3rd Qu.:16.087   3rd Qu.:16.188   3rd Qu.:15.572   3rd Qu.:16.126  
 Max.   :19.059   Max.   :18.814   Max.   :18.84   Max.   :18.583   Max.   :19.043   Max.   :19.094   Max.   :18.786   Max.   :19.063  
                                                                                                                                       
      TM9              TM10             TM11             TM12             VBM1             VBM2            VBM3             VBM4       
 Min.   : 6.836   Min.   : 8.418   Min.   : 7.003   Min.   : 8.624   Min.   : 1.531   Min.   : 1.37   Min.   : 1.336   Min.   : 2.172  
 1st Qu.:11.502   1st Qu.:12.451   1st Qu.:11.602   1st Qu.:12.574   1st Qu.:12.655   1st Qu.:11.85   1st Qu.:11.680   1st Qu.:15.858  
 Median :11.502   Median :12.451   Median :11.602   Median :12.574   Median :12.655   Median :11.85   Median :11.680   Median :15.858  
 Mean   :12.650   Mean   :13.758   Mean   :12.688   Mean   :13.829   Mean   :19.024   Mean   :19.30   Mean   :19.149   Mean   :23.968  
 3rd Qu.:14.669   3rd Qu.:15.934   3rd Qu.:14.802   3rd Qu.:16.099   3rd Qu.:12.655   3rd Qu.:27.77   3rd Qu.:27.521   3rd Qu.:33.787  
 Max.   :18.334   Max.   :18.967   Max.   :18.401   Max.   :19.050   Max.   :62.655   Max.   :61.85   Max.   :61.680   Max.   :65.858  
                                                                                                                                       
      VBM5            VBM6             VBM7             VBM8             VBM9            VBM10            VBM11            VBM12       
 Min.   : 1.40   Min.   : 2.213   Min.   : 2.978   Min.   : 1.933   Min.   : 1.443   Min.   : 2.015   Min.   : 1.554   Min.   : 1.139  
 1st Qu.:12.00   1st Qu.:16.063   1st Qu.:19.889   1st Qu.:14.663   1st Qu.:12.216   1st Qu.:15.075   1st Qu.:12.773   1st Qu.:10.694  
 Median :12.00   Median :16.063   Median :19.889   Median :14.663   Median :12.216   Median :15.075   Median :12.773   Median :10.694  
 Mean   :19.66   Mean   :24.112   Mean   :29.373   Mean   :22.640   Mean   :19.961   Mean   :23.304   Mean   :20.444   Mean   :17.920  
 3rd Qu.:28.00   3rd Qu.:34.094   3rd Qu.:39.834   3rd Qu.:31.994   3rd Qu.:28.324   3rd Qu.:32.612   3rd Qu.:29.159   3rd Qu.:26.040  
 Max.   :62.00   Max.   :66.063   Max.   :69.889   Max.   :64.663   Max.   :62.216   Max.   :65.075   Max.   :62.773   Max.   :60.694  
                                                                                                                                       
      PDM1              PDM2              PDM3             PDM4              PDM5              PDM6              PDM7        
 Min.   :  2.328   Min.   :  1.924   Min.   :  1.84   Min.   :  3.929   Min.   :  2.001   Min.   :  4.031   Min.   :  5.945  
 1st Qu.: 32.797   1st Qu.: 32.555   1st Qu.: 32.50   1st Qu.: 33.757   1st Qu.: 32.601   1st Qu.: 33.819   1st Qu.: 34.967  
 Median : 32.797   Median : 32.555   Median : 32.50   Median : 33.757   Median : 32.601   Median : 33.819   Median : 34.967  
 Mean   : 49.085   Mean   : 45.384   Mean   : 45.65   Mean   : 55.804   Mean   : 46.282   Mean   : 56.079   Mean   : 68.347  
 3rd Qu.: 32.797   3rd Qu.: 59.244   3rd Qu.: 58.40   3rd Qu.: 79.290   3rd Qu.: 60.011   3rd Qu.: 80.313   3rd Qu.: 99.445  
 Max.   :163.275   Max.   :159.244   Max.   :158.40   Max.   :179.290   Max.   :160.011   Max.   :180.313   Max.   :199.445  
                                                                                                                             
      PDM8              PDM9             PDM10             PDM11             PDM12              RLM1            RLM2            RLM3      
 Min.   :  3.331   Min.   :  2.108   Min.   :  3.537   Min.   :  2.386   Min.   :  1.347   Min.   :0.000   Min.   :0.000   Min.   :0.000  
 1st Qu.: 33.399   1st Qu.: 32.665   1st Qu.: 33.522   1st Qu.: 32.832   1st Qu.: 32.208   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
 Median : 33.399   Median : 32.665   Median : 33.522   Median : 32.832   Median : 32.208   Median :1.000   Median :1.000   Median :1.000  
 Mean   : 52.866   Mean   : 47.051   Mean   : 54.494   Mean   : 47.882   Mean   : 42.551   Mean   :1.373   Mean   :1.425   Mean   :1.429  
 3rd Qu.: 73.315   3rd Qu.: 61.080   3rd Qu.: 75.374   3rd Qu.: 63.862   3rd Qu.: 53.468   3rd Qu.:1.000   3rd Qu.:2.000   3rd Qu.:2.000  
 Max.   :173.315   Max.   :161.080   Max.   :175.374   Max.   :163.862   Max.   :153.468   Max.   :4.000   Max.   :4.000   Max.   :4.000  
                                                                                                                                          
      RLM4            RLM5            RLM6            RLM7            RLM8            RLM9           RLM10           RLM11      
 Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
 1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
 Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000  
 Mean   :1.878   Mean   :1.436   Mean   :1.866   Mean   :1.955   Mean   :1.441   Mean   :1.445   Mean   :1.895   Mean   :1.429  
 3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:2.000  
 Max.   :5.000   Max.   :4.000   Max.   :5.000   Max.   :5.000   Max.   :4.000   Max.   :4.000   Max.   :5.000   Max.   :4.000  
                                                                                                                                
     RLM12           PSEM1           PSEM2     
 Min.   :0.000   Min.   :1.500   Min.   :1.50  
 1st Qu.:1.000   1st Qu.:1.750   1st Qu.:1.75  
 Median :1.000   Median :2.000   Median :2.00  
 Mean   :1.419   Mean   :1.892   Mean   :1.90  
 3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.00  
 Max.   :4.000   Max.   :2.000   Max.   :2.00  
                                               
datos.integrados <- datos.alumnos.df
setwd(local.path)
The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
save(datos.integrados, file="datos.integrados.R")
getwd()
[1] "/Users/jos/Downloads/mcc-ad/data"
load("datos.integrados.R")
datos.integrados
head(datos.integrados)
NA

#Separar 100 alumnos que no entraran en Kmeans

set.seed(1234)

ind <- sample(x=c(0,1),size=nrow(datos.integrados),
              replace=TRUE,prob = c(0.9,0.1))
ind
   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  [68] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0
 [135] 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0
 [202] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [269] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0
 [336] 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0
 [403] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [470] 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1
 [537] 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
 [604] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0
 [671] 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0
 [738] 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [805] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1
 [872] 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
 [939] 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0
alumnos.nuevos <- datos.integrados[ind==1,]
alumnos.actuales <- datos.integrados[ind==0,]

alumnos.nuevos
alumnos.actuales
summary(alumnos.nuevos)
 genero admision.letras admision.numeros promedio.preparatoria  edad.ingreso evalucion.socioeconomica nota.conducta   BECA        AM1       
 1:45   Min.   :44.94   Min.   : 4.878   Min.   : 60.00        17     :28    1: 6                     Min.   : 9.00   0:99   Min.   :1.875  
 2:75   1st Qu.:57.09   1st Qu.:29.176   1st Qu.: 61.26        19     :26    2:15                     1st Qu.:14.00   1:21   1st Qu.:1.875  
        Median :59.97   Median :34.947   Median : 69.92        18     :16    3:16                     Median :15.00          Median :1.875  
        Mean   :60.08   Mean   :35.167   Mean   : 72.31        16     :15    4:83                     Mean   :15.52          Mean   :1.875  
        3rd Qu.:63.28   3rd Qu.:41.556   3rd Qu.: 79.83        20     :13                             3rd Qu.:17.00          3rd Qu.:1.875  
        Max.   :72.00   Max.   :58.992   Max.   :100.00        15     :12                             Max.   :20.00          Max.   :1.875  
                                                               (Other):10                                                                   
      AM2             AM3             AM4             AM5             AM6             AM7             AM8             AM9       
 Min.   :1.406   Min.   :1.031   Min.   :1.031   Min.   :1.375   Min.   :1.125   Min.   :1.406   Min.   :1.125   Min.   :1.562  
 1st Qu.:1.875   1st Qu.:1.719   1st Qu.:1.812   1st Qu.:1.906   1st Qu.:1.875   1st Qu.:1.688   1st Qu.:1.938   1st Qu.:1.797  
 Median :1.875   Median :2.000   Median :1.812   Median :1.906   Median :1.875   Median :1.688   Median :1.938   Median :1.844  
 Mean   :1.786   Mean   :1.847   Mean   :1.670   Mean   :1.808   Mean   :1.739   Mean   :1.641   Mean   :1.825   Mean   :1.778  
 3rd Qu.:1.875   3rd Qu.:2.000   3rd Qu.:1.812   3rd Qu.:1.906   3rd Qu.:1.875   3rd Qu.:1.688   3rd Qu.:1.938   3rd Qu.:1.844  
 Max.   :1.875   Max.   :2.000   Max.   :1.812   Max.   :1.906   Max.   :1.875   Max.   :1.688   Max.   :1.938   Max.   :1.844  
                                                                                                                                
      AM10            AM11            AM12            CM1              CM2              CM3              CM4              CM5        
 Min.   :1.219   Min.   :1.562   Min.   :1.438   Min.   : 7.594   Min.   : 8.218   Min.   : 8.439   Min.   : 7.487   Min.   : 8.214  
 1st Qu.:2.000   1st Qu.:1.781   1st Qu.:1.969   1st Qu.:11.956   1st Qu.:12.331   1st Qu.:12.463   1st Qu.:11.892   1st Qu.:12.328  
 Median :2.000   Median :1.969   Median :1.969   Median :11.956   Median :12.331   Median :12.463   Median :11.892   Median :12.328  
 Mean   :1.885   Mean   :1.892   Mean   :1.897   Mean   :12.729   Mean   :13.579   Mean   :13.628   Mean   :13.225   Mean   :13.702  
 3rd Qu.:2.000   3rd Qu.:1.969   3rd Qu.:1.969   3rd Qu.:11.956   3rd Qu.:15.775   3rd Qu.:15.951   3rd Qu.:15.189   3rd Qu.:15.771  
 Max.   :2.000   Max.   :1.969   Max.   :1.969   Max.   :18.638   Max.   :18.887   Max.   :18.976   Max.   :18.595   Max.   :18.885  
                                                                                                                                     
      CM6             CM7              CM8              CM9              CM10             CM11             CM12             TM1        
 Min.   : 9.86   Min.   : 9.467   Min.   : 7.685   Min.   : 6.615   Min.   : 8.036   Min.   : 6.694   Min.   : 8.218   Min.   : 8.648  
 1st Qu.:13.32   1st Qu.:13.080   1st Qu.:12.011   1st Qu.:11.369   1st Qu.:12.221   1st Qu.:11.417   1st Qu.:12.331   1st Qu.:12.589  
 Median :15.20   Median :13.080   Median :12.011   Median :11.369   Median :12.221   Median :11.417   Median :12.331   Median :12.589  
 Mean   :15.15   Mean   :14.247   Mean   :13.263   Mean   :12.507   Mean   :13.653   Mean   :12.172   Mean   :13.711   Mean   :13.355  
 3rd Qu.:17.09   3rd Qu.:16.773   3rd Qu.:15.348   3rd Qu.:14.492   3rd Qu.:15.628   3rd Qu.:14.556   3rd Qu.:15.774   3rd Qu.:12.589  
 Max.   :19.54   Max.   :19.387   Max.   :18.674   Max.   :18.246   Max.   :18.814   Max.   :18.278   Max.   :18.887   Max.   :19.059  
                                                                                                                                       
      TM2              TM3             TM4              TM5              TM6              TM7              TM8              TM9        
 Min.   : 8.036   Min.   : 8.11   Min.   : 7.457   Min.   : 8.608   Min.   : 8.735   Min.   : 7.965   Min.   : 8.657   Min.   : 6.836  
 1st Qu.:12.221   1st Qu.:12.27   1st Qu.:11.874   1st Qu.:12.565   1st Qu.:12.641   1st Qu.:12.179   1st Qu.:12.594   1st Qu.:11.502  
 Median :12.221   Median :12.27   Median :11.874   Median :12.565   Median :14.415   Median :12.179   Median :12.594   Median :11.502  
 Mean   :13.448   Mean   :13.40   Mean   :13.204   Mean   :13.986   Mean   :14.364   Mean   :13.151   Mean   :13.943   Mean   :12.666  
 3rd Qu.:15.629   3rd Qu.:15.69   3rd Qu.:15.166   3rd Qu.:16.087   3rd Qu.:16.188   3rd Qu.:15.572   3rd Qu.:16.126   3rd Qu.:14.669  
 Max.   :18.814   Max.   :18.84   Max.   :18.583   Max.   :19.043   Max.   :19.094   Max.   :18.786   Max.   :19.063   Max.   :18.334  
                                                                                                                                       
      TM10             TM11             TM12             VBM1             VBM2            VBM3             VBM4             VBM5      
 Min.   : 8.418   Min.   : 7.003   Min.   : 8.624   Min.   : 1.531   Min.   : 1.37   Min.   : 1.336   Min.   : 2.172   Min.   : 1.40  
 1st Qu.:12.451   1st Qu.:11.602   1st Qu.:12.574   1st Qu.:12.655   1st Qu.:11.85   1st Qu.:11.680   1st Qu.:15.858   1st Qu.:12.00  
 Median :12.451   Median :11.602   Median :12.574   Median :12.655   Median :11.85   Median :11.680   Median :15.858   Median :12.00  
 Mean   :13.918   Mean   :12.397   Mean   :14.001   Mean   :18.942   Mean   :19.67   Mean   :19.901   Mean   :24.723   Mean   :20.29  
 3rd Qu.:15.934   3rd Qu.:14.802   3rd Qu.:16.099   3rd Qu.:12.655   3rd Qu.:27.77   3rd Qu.:27.521   3rd Qu.:33.787   3rd Qu.:28.00  
 Max.   :18.967   Max.   :18.401   Max.   :19.050   Max.   :62.655   Max.   :61.85   Max.   :61.680   Max.   :65.858   Max.   :62.00  
                                                                                                                                      
      VBM6             VBM7             VBM8             VBM9            VBM10            VBM11            VBM12             PDM1        
 Min.   : 2.213   Min.   : 2.978   Min.   : 1.933   Min.   : 1.443   Min.   : 2.015   Min.   : 1.554   Min.   : 1.139   Min.   :  2.328  
 1st Qu.:16.063   1st Qu.:19.889   1st Qu.:14.663   1st Qu.:12.216   1st Qu.:15.075   1st Qu.:12.773   1st Qu.:10.694   1st Qu.: 32.797  
 Median :25.078   Median :19.889   Median :14.663   Median :12.216   Median :15.075   Median :12.773   Median :10.694   Median : 32.797  
 Mean   :26.704   Mean   :27.247   Mean   :22.746   Mean   :20.068   Mean   :24.151   Mean   :20.014   Mean   :18.761   Mean   : 48.853  
 3rd Qu.:34.094   3rd Qu.:39.834   3rd Qu.:31.994   3rd Qu.:28.324   3rd Qu.:32.612   3rd Qu.:29.159   3rd Qu.:26.040   3rd Qu.: 32.797  
 Max.   :66.063   Max.   :69.889   Max.   :64.663   Max.   :62.216   Max.   :65.075   Max.   :62.773   Max.   :60.694   Max.   :163.275  
                                                                                                                                         
      PDM2              PDM3             PDM4              PDM5              PDM6              PDM7              PDM8        
 Min.   :  1.924   Min.   :  1.84   Min.   :  3.929   Min.   :  2.001   Min.   :  4.031   Min.   :  5.945   Min.   :  3.331  
 1st Qu.: 32.555   1st Qu.: 32.50   1st Qu.: 33.757   1st Qu.: 32.601   1st Qu.: 33.819   1st Qu.: 34.967   1st Qu.: 33.399  
 Median : 32.555   Median : 32.50   Median : 33.757   Median : 32.601   Median : 57.066   Median : 34.967   Median : 33.399  
 Mean   : 46.181   Mean   : 48.16   Mean   : 57.651   Mean   : 47.534   Mean   : 63.165   Mean   : 62.721   Mean   : 53.190  
 3rd Qu.: 59.244   3rd Qu.: 58.40   3rd Qu.: 79.290   3rd Qu.: 60.011   3rd Qu.: 80.313   3rd Qu.: 99.445   3rd Qu.: 73.315  
 Max.   :159.244   Max.   :158.40   Max.   :179.290   Max.   :160.011   Max.   :180.313   Max.   :199.445   Max.   :173.315  
                                                                                                                             
      PDM9             PDM10             PDM11             PDM12              RLM1            RLM2            RLM3            RLM4      
 Min.   :  2.108   Min.   :  3.537   Min.   :  2.386   Min.   :  1.347   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
 1st Qu.: 32.665   1st Qu.: 33.522   1st Qu.: 32.832   1st Qu.: 32.208   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
 Median : 32.665   Median : 33.522   Median : 32.832   Median : 32.208   Median :1.000   Median :1.000   Median :1.000   Median :1.000  
 Mean   : 47.036   Mean   : 56.773   Mean   : 47.359   Mean   : 44.220   Mean   :1.367   Mean   :1.442   Mean   :1.458   Mean   :1.958  
 3rd Qu.: 61.080   3rd Qu.: 75.374   3rd Qu.: 63.862   3rd Qu.: 53.468   3rd Qu.:1.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:3.000  
 Max.   :161.080   Max.   :175.374   Max.   :163.862   Max.   :153.468   Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :5.000  
                                                                                                                                        
      RLM5            RLM6            RLM7          RLM8           RLM9          RLM10           RLM11           RLM12           PSEM1     
 Min.   :0.000   Min.   :0.000   Min.   :0.0   Min.   :0.00   Min.   :0.00   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :1.50  
 1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.0   1st Qu.:1.00   1st Qu.:1.00   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.75  
 Median :1.000   Median :2.000   Median :1.0   Median :1.00   Median :1.00   Median :1.000   Median :1.000   Median :1.000   Median :2.00  
 Mean   :1.475   Mean   :2.092   Mean   :1.8   Mean   :1.45   Mean   :1.45   Mean   :1.958   Mean   :1.383   Mean   :1.475   Mean   :1.89  
 3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:3.0   3rd Qu.:2.00   3rd Qu.:2.00   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.00  
 Max.   :4.000   Max.   :5.000   Max.   :5.0   Max.   :4.00   Max.   :4.00   Max.   :5.000   Max.   :4.000   Max.   :4.000   Max.   :2.00  
                                                                                                                                           
     PSEM2      
 Min.   :1.500  
 1st Qu.:1.750  
 Median :2.000  
 Mean   :1.923  
 3rd Qu.:2.000  
 Max.   :2.000  
                
summary(alumnos.actuales)
 genero  admision.letras admision.numeros promedio.preparatoria  edad.ingreso evalucion.socioeconomica nota.conducta   BECA   
 1:360   Min.   :44.99   Min.   : 4.986   Min.   : 60.00        17     :172   1: 50                    Min.   : 9.00   0:738  
 2:520   1st Qu.:56.59   1st Qu.:28.187   1st Qu.: 60.00        18     :151   2: 92                    1st Qu.:14.00   1:142  
         Median :60.04   Median :35.076   Median : 70.11        19     :140   3:136                    Median :15.50          
         Mean   :60.05   Mean   :35.107   Mean   : 72.24        16     :125   4:602                    Mean   :15.53          
         3rd Qu.:63.67   3rd Qu.:42.335   3rd Qu.: 81.00        20     :100                            3rd Qu.:17.00          
         Max.   :77.71   Max.   :70.411   Max.   :100.00        15     : 86                            Max.   :20.00          
                                                                (Other):106                                                   
      AM1             AM2             AM3             AM4             AM5             AM6             AM7             AM8       
 Min.   :1.875   Min.   :1.406   Min.   :1.031   Min.   :1.031   Min.   :1.375   Min.   :1.125   Min.   :1.406   Min.   :1.125  
 1st Qu.:1.875   1st Qu.:1.875   1st Qu.:1.719   1st Qu.:1.406   1st Qu.:1.562   1st Qu.:1.438   1st Qu.:1.594   1st Qu.:1.688  
 Median :1.875   Median :1.875   Median :2.000   Median :1.812   Median :1.906   Median :1.875   Median :1.688   Median :1.938  
 Mean   :1.875   Mean   :1.778   Mean   :1.836   Mean   :1.656   Mean   :1.793   Mean   :1.717   Mean   :1.631   Mean   :1.798  
 3rd Qu.:1.875   3rd Qu.:1.875   3rd Qu.:2.000   3rd Qu.:1.812   3rd Qu.:1.906   3rd Qu.:1.875   3rd Qu.:1.688   3rd Qu.:1.938  
 Max.   :1.875   Max.   :1.875   Max.   :2.000   Max.   :1.812   Max.   :1.906   Max.   :1.875   Max.   :1.688   Max.   :1.938  
                                                                                                                                
      AM9             AM10            AM11            AM12            CM1              CM2              CM3              CM4        
 Min.   :1.562   Min.   :1.219   Min.   :1.562   Min.   :1.438   Min.   : 7.594   Min.   : 8.218   Min.   : 8.439   Min.   : 7.487  
 1st Qu.:1.797   1st Qu.:2.000   1st Qu.:1.969   1st Qu.:1.969   1st Qu.:11.956   1st Qu.:12.331   1st Qu.:12.463   1st Qu.:11.892  
 Median :1.844   Median :2.000   Median :1.969   Median :1.969   Median :11.956   Median :12.331   Median :12.463   Median :11.892  
 Mean   :1.781   Mean   :1.870   Mean   :1.892   Mean   :1.872   Mean   :12.754   Mean   :13.566   Mean   :13.711   Mean   :13.042  
 3rd Qu.:1.844   3rd Qu.:2.000   3rd Qu.:1.969   3rd Qu.:1.969   3rd Qu.:11.956   3rd Qu.:15.775   3rd Qu.:15.951   3rd Qu.:15.189  
 Max.   :1.844   Max.   :2.000   Max.   :1.969   Max.   :1.969   Max.   :18.638   Max.   :18.887   Max.   :18.976   Max.   :18.595  
                                                                                                                                    
      CM5              CM6             CM7              CM8              CM9              CM10             CM11             CM12       
 Min.   : 8.214   Min.   : 9.86   Min.   : 9.467   Min.   : 7.685   Min.   : 6.615   Min.   : 8.036   Min.   : 6.694   Min.   : 8.218  
 1st Qu.:12.328   1st Qu.:13.32   1st Qu.:13.080   1st Qu.:12.011   1st Qu.:11.369   1st Qu.:12.221   1st Qu.:11.417   1st Qu.:12.331  
 Median :12.328   Median :13.32   Median :13.080   Median :12.011   Median :11.369   Median :12.221   Median :11.417   Median :12.331  
 Mean   :13.566   Mean   :14.67   Mean   :14.648   Mean   :13.229   Mean   :12.491   Mean   :13.466   Mean   :12.506   Mean   :13.516  
 3rd Qu.:15.771   3rd Qu.:17.09   3rd Qu.:16.773   3rd Qu.:15.348   3rd Qu.:14.492   3rd Qu.:15.628   3rd Qu.:14.556   3rd Qu.:15.774  
 Max.   :18.885   Max.   :19.54   Max.   :19.387   Max.   :18.674   Max.   :18.246   Max.   :18.814   Max.   :18.278   Max.   :18.887  
                                                                                                                                       
      TM1              TM2              TM3             TM4              TM5              TM6              TM7              TM8        
 Min.   : 8.648   Min.   : 8.036   Min.   : 8.11   Min.   : 7.457   Min.   : 8.608   Min.   : 8.735   Min.   : 7.965   Min.   : 8.657  
 1st Qu.:12.589   1st Qu.:12.221   1st Qu.:12.27   1st Qu.:11.874   1st Qu.:12.565   1st Qu.:12.641   1st Qu.:12.179   1st Qu.:12.594  
 Median :12.589   Median :12.221   Median :12.27   Median :11.874   Median :12.565   Median :12.641   Median :12.179   Median :12.594  
 Mean   :13.377   Mean   :13.435   Mean   :13.48   Mean   :13.021   Mean   :13.849   Mean   :13.871   Mean   :13.591   Mean   :13.917  
 3rd Qu.:12.589   3rd Qu.:15.629   3rd Qu.:15.69   3rd Qu.:15.166   3rd Qu.:16.087   3rd Qu.:16.188   3rd Qu.:15.572   3rd Qu.:16.126  
 Max.   :19.059   Max.   :18.814   Max.   :18.84   Max.   :18.583   Max.   :19.043   Max.   :19.094   Max.   :18.786   Max.   :19.063  
                                                                                                                                       
      TM9              TM10             TM11             TM12             VBM1             VBM2            VBM3             VBM4       
 Min.   : 6.836   Min.   : 8.418   Min.   : 7.003   Min.   : 8.624   Min.   : 1.531   Min.   : 1.37   Min.   : 1.336   Min.   : 2.172  
 1st Qu.:11.502   1st Qu.:12.451   1st Qu.:11.602   1st Qu.:12.574   1st Qu.:12.655   1st Qu.:11.85   1st Qu.:11.680   1st Qu.:15.858  
 Median :11.502   Median :12.451   Median :11.602   Median :12.574   Median :12.655   Median :11.85   Median :11.680   Median :15.858  
 Mean   :12.648   Mean   :13.737   Mean   :12.728   Mean   :13.806   Mean   :19.036   Mean   :19.24   Mean   :19.046   Mean   :23.866  
 3rd Qu.:14.669   3rd Qu.:15.934   3rd Qu.:14.802   3rd Qu.:16.099   3rd Qu.:12.655   3rd Qu.:27.77   3rd Qu.:27.521   3rd Qu.:33.787  
 Max.   :18.334   Max.   :18.967   Max.   :18.401   Max.   :19.050   Max.   :62.655   Max.   :61.85   Max.   :61.680   Max.   :65.858  
                                                                                                                                       
      VBM5            VBM6             VBM7             VBM8             VBM9            VBM10            VBM11            VBM12       
 Min.   : 1.40   Min.   : 2.213   Min.   : 2.978   Min.   : 1.933   Min.   : 1.443   Min.   : 2.015   Min.   : 1.554   Min.   : 1.139  
 1st Qu.:12.00   1st Qu.:16.063   1st Qu.:19.889   1st Qu.:14.663   1st Qu.:12.216   1st Qu.:15.075   1st Qu.:12.773   1st Qu.:10.694  
 Median :12.00   Median :16.063   Median :19.889   Median :14.663   Median :12.216   Median :15.075   Median :12.773   Median :10.694  
 Mean   :19.57   Mean   :23.758   Mean   :29.663   Mean   :22.626   Mean   :19.946   Mean   :23.188   Mean   :20.502   Mean   :17.805  
 3rd Qu.:28.00   3rd Qu.:34.094   3rd Qu.:39.834   3rd Qu.:31.994   3rd Qu.:28.324   3rd Qu.:32.612   3rd Qu.:29.159   3rd Qu.:26.040  
 Max.   :62.00   Max.   :66.063   Max.   :69.889   Max.   :64.663   Max.   :62.216   Max.   :65.075   Max.   :62.773   Max.   :60.694  
                                                                                                                                       
      PDM1              PDM2              PDM3             PDM4              PDM5              PDM6              PDM7        
 Min.   :  2.328   Min.   :  1.924   Min.   :  1.84   Min.   :  3.929   Min.   :  2.001   Min.   :  4.031   Min.   :  5.945  
 1st Qu.: 32.797   1st Qu.: 32.555   1st Qu.: 32.50   1st Qu.: 33.757   1st Qu.: 32.601   1st Qu.: 33.819   1st Qu.: 34.967  
 Median : 32.797   Median : 32.555   Median : 32.50   Median : 33.757   Median : 32.601   Median : 33.819   Median : 34.967  
 Mean   : 49.116   Mean   : 45.275   Mean   : 45.31   Mean   : 55.552   Mean   : 46.111   Mean   : 55.113   Mean   : 69.114  
 3rd Qu.: 32.797   3rd Qu.: 59.244   3rd Qu.: 58.40   3rd Qu.: 79.290   3rd Qu.: 60.011   3rd Qu.: 80.313   3rd Qu.: 99.445  
 Max.   :163.275   Max.   :159.244   Max.   :158.40   Max.   :179.290   Max.   :160.011   Max.   :180.313   Max.   :199.445  
                                                                                                                             
      PDM8              PDM9             PDM10             PDM11             PDM12              RLM1            RLM2            RLM3      
 Min.   :  3.331   Min.   :  2.108   Min.   :  3.537   Min.   :  2.386   Min.   :  1.347   Min.   :0.000   Min.   :0.000   Min.   :0.000  
 1st Qu.: 33.399   1st Qu.: 32.665   1st Qu.: 33.522   1st Qu.: 32.832   1st Qu.: 32.208   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
 Median : 33.399   Median : 32.665   Median : 33.522   Median : 32.832   Median : 32.208   Median :1.000   Median :1.000   Median :1.000  
 Mean   : 52.821   Mean   : 47.053   Mean   : 54.183   Mean   : 47.953   Mean   : 42.323   Mean   :1.374   Mean   :1.423   Mean   :1.425  
 3rd Qu.: 73.315   3rd Qu.: 61.080   3rd Qu.: 75.374   3rd Qu.: 63.862   3rd Qu.: 53.468   3rd Qu.:1.000   3rd Qu.:2.000   3rd Qu.:2.000  
 Max.   :173.315   Max.   :161.080   Max.   :175.374   Max.   :163.862   Max.   :153.468   Max.   :4.000   Max.   :4.000   Max.   :4.000  
                                                                                                                                          
      RLM4            RLM5            RLM6            RLM7            RLM8           RLM9           RLM10           RLM11      
 Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.00   Min.   :0.000   Min.   :0.000   Min.   :0.000  
 1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.00   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
 Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.00   Median :1.000   Median :1.000   Median :1.000  
 Mean   :1.867   Mean   :1.431   Mean   :1.835   Mean   :1.976   Mean   :1.44   Mean   :1.444   Mean   :1.886   Mean   :1.435  
 3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:3.000   3rd Qu.:2.00   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:2.000  
 Max.   :5.000   Max.   :4.000   Max.   :5.000   Max.   :5.000   Max.   :4.00   Max.   :4.000   Max.   :5.000   Max.   :4.000  
                                                                                                                               
     RLM12           PSEM1           PSEM2      
 Min.   :0.000   Min.   :1.500   Min.   :1.500  
 1st Qu.:1.000   1st Qu.:1.750   1st Qu.:1.750  
 Median :1.000   Median :2.000   Median :2.000  
 Mean   :1.411   Mean   :1.893   Mean   :1.897  
 3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000  
 Max.   :4.000   Max.   :2.000   Max.   :2.000  
                                                
set.seed(1234)

ind <- sample(x=c(0,1),size=nrow(alumnos.actuales),
              replace=TRUE,prob = c(0.7,0.3))
ind
  [1] 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0
 [69] 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0
[137] 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 1 0 0 0 0 1
[205] 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1
[273] 0 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 0
[341] 1 0 0 1 0 0 0 0 1 0 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0
[409] 0 0 1 1 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1
[477] 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 1 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0
[545] 1 0 1 1 1 1 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
[613] 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1
[681] 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0
[749] 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0
[817] 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 1 1 0 1 0 0 0 0 0
alumnos.training <- alumnos.actuales[ind==0,]
alumnos.test <- alumnos.actuales[ind==1,]

str(alumnos.training)
'data.frame':   613 obs. of  82 variables:
 $ genero                  : Factor w/ 2 levels "1","2": 2 2 2 1 2 2 2 1 2 1 ...
 $ admision.letras         : num  60.1 59.1 53.1 57 61.9 ...
 $ admision.numeros        : num  35.2 33.2 21.3 29 38.9 ...
 $ promedio.preparatoria   : num  70.3 67.2 60 61 75.8 ...
 $ edad.ingreso            : Factor w/ 15 levels "11","12","13",..: 8 7 5 6 8 5 7 4 7 10 ...
 $ evalucion.socioeconomica: Factor w/ 4 levels "1","2","3","4": 4 4 4 4 4 4 4 4 4 4 ...
 $ nota.conducta           : num  16 15 13 14 16 13 15 12 15 18 ...
 $ BECA                    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ AM1                     : num  1.88 1.88 1.88 1.88 1.88 ...
 $ AM2                     : num  1.88 1.41 1.41 1.88 1.53 ...
 $ AM3                     : num  2 2 2 2 2 ...
 $ AM4                     : num  1.41 1.81 1.81 1.81 1.81 ...
 $ AM5                     : num  1.91 1.5 1.38 1.56 1.56 ...
 $ AM6                     : num  1.44 1.38 1.12 1.88 1.12 ...
 $ AM7                     : num  1.59 1.69 1.69 1.69 1.59 ...
 $ AM8                     : num  1.12 1.12 1.94 1.69 1.94 ...
 $ AM9                     : num  1.84 1.84 1.84 1.66 1.84 ...
 $ AM10                    : num  2 2 1.22 2 2 ...
 $ AM11                    : num  1.97 1.97 1.97 1.97 1.97 ...
 $ AM12                    : num  1.97 1.97 1.97 1.97 1.78 ...
 $ CM1                     : num  12 12 12 12 12 ...
 $ CM2                     : num  12.33 12.33 15.77 15.77 8.22 ...
 $ CM3                     : num  12.5 12.5 12.5 16 16 ...
 $ CM4                     : num  15.2 11.9 11.9 15.2 15.2 ...
 $ CM5                     : num  12.3 15.8 12.3 12.3 12.3 ...
 $ CM6                     : num  17.1 17.1 13.3 17.1 19.5 ...
 $ CM7                     : num  9.47 13.08 13.08 13.08 19.39 ...
 $ CM8                     : num  12.01 15.35 12.01 7.69 12.01 ...
 $ CM9                     : num  11.4 11.4 11.4 18.2 11.4 ...
 $ CM10                    : num  12.2 12.2 12.2 15.6 15.6 ...
 $ CM11                    : num  11.4 11.4 11.4 11.4 11.4 ...
 $ CM12                    : num  12.33 12.33 12.33 15.77 8.22 ...
 $ TM1                     : num  12.6 12.6 12.6 12.6 12.6 ...
 $ TM2                     : num  12.22 12.22 15.63 15.63 8.04 ...
 $ TM3                     : num  12.3 12.3 12.3 15.7 15.7 ...
 $ TM4                     : num  15.2 11.9 11.9 15.2 15.2 ...
 $ TM5                     : num  12.6 16.1 12.6 12.6 12.6 ...
 $ TM6                     : num  16.2 16.2 12.6 16.2 19.1 ...
 $ TM7                     : num  7.97 12.18 12.18 12.18 18.79 ...
 $ TM8                     : num  12.59 16.13 12.59 8.66 12.59 ...
 $ TM9                     : num  11.5 11.5 11.5 18.3 11.5 ...
 $ TM10                    : num  12.5 12.5 12.5 15.9 15.9 ...
 $ TM11                    : num  11.6 11.6 11.6 11.6 11.6 ...
 $ TM12                    : num  12.57 12.57 12.57 16.1 8.62 ...
 $ VBM1                    : num  12.7 12.7 12.7 12.7 12.7 ...
 $ VBM2                    : num  11.85 11.85 27.77 27.77 1.37 ...
 $ VBM3                    : num  11.7 11.7 11.7 27.5 27.5 ...
 $ VBM4                    : num  33.8 15.9 15.9 33.8 33.8 ...
 $ VBM5                    : num  12 28 12 12 12 ...
 $ VBM6                    : num  34.1 34.1 16.1 34.1 66.1 ...
 $ VBM7                    : num  2.98 19.89 19.89 19.89 69.89 ...
 $ VBM8                    : num  14.66 31.99 14.66 1.93 14.66 ...
 $ VBM9                    : num  12.2 12.2 12.2 62.2 12.2 ...
 $ VBM10                   : num  15.1 15.1 15.1 32.6 32.6 ...
 $ VBM11                   : num  12.8 12.8 12.8 12.8 12.8 ...
 $ VBM12                   : num  10.69 10.69 10.69 26.04 1.14 ...
 $ PDM1                    : num  32.8 32.8 32.8 32.8 32.8 ...
 $ PDM2                    : num  32.55 32.55 59.24 59.24 1.92 ...
 $ PDM3                    : num  32.5 32.5 32.5 58.4 58.4 ...
 $ PDM4                    : num  79.3 33.8 33.8 79.3 79.3 ...
 $ PDM5                    : num  32.6 60 32.6 32.6 32.6 ...
 $ PDM6                    : num  80.3 80.3 33.8 80.3 180.3 ...
 $ PDM7                    : num  5.94 34.97 34.97 34.97 199.45 ...
 $ PDM8                    : num  33.4 73.31 33.4 3.33 33.4 ...
 $ PDM9                    : num  32.7 32.7 32.7 161.1 32.7 ...
 $ PDM10                   : num  33.5 33.5 33.5 75.4 75.4 ...
 $ PDM11                   : num  32.8 32.8 32.8 32.8 32.8 ...
 $ PDM12                   : num  32.21 32.21 32.21 53.47 1.35 ...
 $ RLM1                    : num  1 1 1 1 1 4 2 1 1 1 ...
 $ RLM2                    : num  1 1 2 2 0 2 1 1 2 1 ...
 $ RLM3                    : num  1 1 1 2 2 2 2 1 2 1 ...
 $ RLM4                    : num  3 1 1 3 3 3 3 1 1 1 ...
 $ RLM5                    : num  1 2 1 1 1 2 1 1 2 1 ...
 $ RLM6                    : num  3 3 1 3 5 0 3 1 3 0 ...
 $ RLM7                    : num  0 1 1 1 5 3 1 1 3 1 ...
 $ RLM8                    : num  1 2 1 0 1 4 0 1 2 1 ...
 $ RLM9                    : num  1 1 1 4 1 2 2 1 1 2 ...
 $ RLM10                   : num  1 1 1 3 3 3 3 5 3 1 ...
 $ RLM11                   : num  1 1 1 1 1 2 1 1 1 1 ...
 $ RLM12                   : num  1 1 1 2 0 2 1 1 1 1 ...
 $ PSEM1                   : num  2 2 2 2 2 1.5 2 2 2 2 ...
 $ PSEM2                   : num  2 1.5 1.5 2 1.5 2 2 2 2 2 ...
set.seed(2020)
wss.alumnos <-vector()
wss.alumnos
logical(0)
centroides.alumnos <- 25
for ( i in 1:centroides.alumnos ) wss.alumnos[i] <- sum(kmeans(alumnos.training,centers = i)$withinss)

#plot
plot(1:centroides.alumnos  , wss.alumnos , type="b", xlab="Numer de clusters", ylab="Error standard")

Analísis de hombro, brazo, codo para seleccionar el centroide

imgPath.codo <- paste(local.path.imgs,"/Kmeans-codo-alumnos.png",sep = "")


img.codo.alumnos <- readPNG(imgPath.codo)
plot.new()
rasterImage(img.codo.alumnos,0,0,1,1)

set.seed(2020)
wss.alumnos <-vector()
wss.alumnos
logical(0)
centroides.alumnos <- 10
for ( i in 1:centroides.alumnos ) wss.alumnos[i] <- sum(kmeans(alumnos.training,centers = i)$withinss)

#plot
plot(1:centroides.alumnos  , wss.alumnos , type="b", xlab="Numer de clusters", ylab="Error standard")

imgPath.codo.seleccionado <- paste(local.path.imgs,"/Kmeans-codo-alumnos-seleccionado.png",sep = "")


img.codo.sel.alumnos <- readPNG(imgPath.codo.seleccionado)
plot.new()
rasterImage(img.codo.sel.alumnos,0,0,1,1)

clustering.kmeans <- kmeans(x=alumnos.training, centers = 4)
clustering.kmeans$withinss
[1]  867289.6 2325698.5 1619780.3 3420804.7
alumnos.training$genero <- as.numeric(alumnos.training$genero)
alumnos.training$edad.ingreso <- as.numeric(alumnos.training$edad.ingreso)
alumnos.training$evalucion.socioeconomica <- as.numeric(alumnos.training$evalucion.socioeconomica)
alumnos.training$BECA <- as.numeric(alumnos.training$BECA)
sum(is.na(alumnos.training))
[1] 0
#str(alumnos.training)
datos.alumnos.df$genero <- as.numeric(datos.alumnos.df$genero)
datos.alumnos.df$edad.ingreso <- as.numeric(datos.alumnos.df$edad.ingreso)
datos.alumnos.df$BECA <- as.numeric(datos.alumnos.df$BECA)
datos.alumnos.df$evalucion.socioeconomica <- as.numeric(datos.alumnos.df$evalucion.socioeconomica)
str(datos.alumnos.df)
'data.frame':   1000 obs. of  82 variables:
 $ genero                  : num  2 2 2 1 2 2 2 2 1 2 ...
 $ admision.letras         : num  60.1 59.1 53.1 57 61.5 ...
 $ admision.numeros        : num  35.2 33.2 21.3 29 37.9 ...
 $ promedio.preparatoria   : num  70.3 67.2 60 61 74.4 ...
 $ edad.ingreso            : num  8 7 5 6 8 8 5 7 4 7 ...
 $ evalucion.socioeconomica: num  4 4 4 4 4 4 4 4 4 4 ...
 $ nota.conducta           : num  16 15 13 14 16 16 13 15 12 15 ...
 $ BECA                    : num  1 1 1 1 1 1 1 1 1 1 ...
 $ AM1                     : num  1.88 1.88 1.88 1.88 1.88 ...
 $ AM2                     : num  1.88 1.41 1.41 1.88 1.41 ...
 $ AM3                     : num  2 2 2 2 1.72 ...
 $ AM4                     : num  1.41 1.81 1.81 1.81 1.81 ...
 $ AM5                     : num  1.91 1.5 1.38 1.56 1.91 ...
 $ AM6                     : num  1.44 1.38 1.12 1.88 1.12 ...
 $ AM7                     : num  1.59 1.69 1.69 1.69 1.69 ...
 $ AM8                     : num  1.12 1.12 1.94 1.69 1.12 ...
 $ AM9                     : num  1.84 1.84 1.84 1.66 1.66 ...
 $ AM10                    : num  2 2 1.22 2 2 ...
 $ AM11                    : num  1.97 1.97 1.97 1.97 1.97 ...
 $ AM12                    : num  1.97 1.97 1.97 1.97 1.97 ...
 $ CM1                     : num  12 12 12 12 12 ...
 $ CM2                     : num  12.3 12.3 15.8 15.8 12.3 ...
 $ CM3                     : num  12.5 12.5 12.5 16 16 ...
 $ CM4                     : num  15.2 11.9 11.9 15.2 11.9 ...
 $ CM5                     : num  12.3 15.8 12.3 12.3 12.3 ...
 $ CM6                     : num  17.1 17.1 13.3 17.1 13.3 ...
 $ CM7                     : num  9.47 13.08 13.08 13.08 13.08 ...
 $ CM8                     : num  12.01 15.35 12.01 7.69 15.35 ...
 $ CM9                     : num  11.37 11.37 11.37 18.25 6.61 ...
 $ CM10                    : num  12.2 12.2 12.2 15.6 12.2 ...
 $ CM11                    : num  11.4 11.4 11.4 11.4 11.4 ...
 $ CM12                    : num  12.3 12.3 12.3 15.8 12.3 ...
 $ TM1                     : num  12.6 12.6 12.6 12.6 12.6 ...
 $ TM2                     : num  12.2 12.2 15.6 15.6 12.2 ...
 $ TM3                     : num  12.3 12.3 12.3 15.7 15.7 ...
 $ TM4                     : num  15.2 11.9 11.9 15.2 11.9 ...
 $ TM5                     : num  12.6 16.1 12.6 12.6 12.6 ...
 $ TM6                     : num  16.2 16.2 12.6 16.2 12.6 ...
 $ TM7                     : num  7.97 12.18 12.18 12.18 12.18 ...
 $ TM8                     : num  12.59 16.13 12.59 8.66 16.13 ...
 $ TM9                     : num  11.5 11.5 11.5 18.33 6.84 ...
 $ TM10                    : num  12.5 12.5 12.5 15.9 12.5 ...
 $ TM11                    : num  11.6 11.6 11.6 11.6 11.6 ...
 $ TM12                    : num  12.6 12.6 12.6 16.1 12.6 ...
 $ VBM1                    : num  12.7 12.7 12.7 12.7 12.7 ...
 $ VBM2                    : num  11.8 11.8 27.8 27.8 11.8 ...
 $ VBM3                    : num  11.7 11.7 11.7 27.5 27.5 ...
 $ VBM4                    : num  33.8 15.9 15.9 33.8 15.9 ...
 $ VBM5                    : num  12 28 12 12 12 ...
 $ VBM6                    : num  34.1 34.1 16.1 34.1 16.1 ...
 $ VBM7                    : num  2.98 19.89 19.89 19.89 19.89 ...
 $ VBM8                    : num  14.66 31.99 14.66 1.93 31.99 ...
 $ VBM9                    : num  12.22 12.22 12.22 62.22 1.44 ...
 $ VBM10                   : num  15.1 15.1 15.1 32.6 15.1 ...
 $ VBM11                   : num  12.8 12.8 12.8 12.8 12.8 ...
 $ VBM12                   : num  10.7 10.7 10.7 26 10.7 ...
 $ PDM1                    : num  32.8 32.8 32.8 32.8 32.8 ...
 $ PDM2                    : num  32.6 32.6 59.2 59.2 32.6 ...
 $ PDM3                    : num  32.5 32.5 32.5 58.4 58.4 ...
 $ PDM4                    : num  79.3 33.8 33.8 79.3 33.8 ...
 $ PDM5                    : num  32.6 60 32.6 32.6 32.6 ...
 $ PDM6                    : num  80.3 80.3 33.8 80.3 33.8 ...
 $ PDM7                    : num  5.94 34.97 34.97 34.97 34.97 ...
 $ PDM8                    : num  33.4 73.31 33.4 3.33 73.31 ...
 $ PDM9                    : num  32.66 32.66 32.66 161.08 2.11 ...
 $ PDM10                   : num  33.5 33.5 33.5 75.4 33.5 ...
 $ PDM11                   : num  32.8 32.8 32.8 32.8 32.8 ...
 $ PDM12                   : num  32.2 32.2 32.2 53.5 32.2 ...
 $ RLM1                    : num  1 1 1 1 1 1 4 2 1 1 ...
 $ RLM2                    : num  1 1 2 2 1 0 2 1 1 2 ...
 $ RLM3                    : num  1 1 1 2 2 2 2 2 1 2 ...
 $ RLM4                    : num  3 1 1 3 1 3 3 3 1 1 ...
 $ RLM5                    : num  1 2 1 1 1 1 2 1 1 2 ...
 $ RLM6                    : num  3 3 1 3 1 5 0 3 1 3 ...
 $ RLM7                    : num  0 1 1 1 1 5 3 1 1 3 ...
 $ RLM8                    : num  1 2 1 0 2 1 4 0 1 2 ...
 $ RLM9                    : num  1 1 1 4 0 1 2 2 1 1 ...
 $ RLM10                   : num  1 1 1 3 1 3 3 3 5 3 ...
 $ RLM11                   : num  1 1 1 1 1 1 2 1 1 1 ...
 $ RLM12                   : num  1 1 1 2 1 0 2 1 1 1 ...
 $ PSEM1                   : num  2 2 2 2 2 2 1.5 2 2 2 ...
 $ PSEM2                   : num  2 1.5 1.5 2 2 1.5 2 2 2 2 ...
#ds.test <- data.frame()
#ds.test <- cbind(datos.alumnos.df$genero, datos.alumnos.df$CM1)
#str(ds.test)
sum(datos.alumnos.df$AM11)
[1] 1892.344
colnames(datos.alumnos.df)
 [1] "genero"                   "admision.letras"          "admision.numeros"         "promedio.preparatoria"    "edad.ingreso"             "evalucion.socioeconomica" "nota.conducta"            "BECA"                    
 [9] "AM1"                      "AM2"                      "AM3"                      "AM4"                      "AM5"                      "AM6"                      "AM7"                      "AM8"                     
[17] "AM9"                      "AM10"                     "AM11"                     "AM12"                     "CM1"                      "CM2"                      "CM3"                      "CM4"                     
[25] "CM5"                      "CM6"                      "CM7"                      "CM8"                      "CM9"                      "CM10"                     "CM11"                     "CM12"                    
[33] "TM1"                      "TM2"                      "TM3"                      "TM4"                      "TM5"                      "TM6"                      "TM7"                      "TM8"                     
[41] "TM9"                      "TM10"                     "TM11"                     "TM12"                     "VBM1"                     "VBM2"                     "VBM3"                     "VBM4"                    
[49] "VBM5"                     "VBM6"                     "VBM7"                     "VBM8"                     "VBM9"                     "VBM10"                    "VBM11"                    "VBM12"                   
[57] "PDM1"                     "PDM2"                     "PDM3"                     "PDM4"                     "PDM5"                     "PDM6"                     "PDM7"                     "PDM8"                    
[65] "PDM9"                     "PDM10"                    "PDM11"                    "PDM12"                    "RLM1"                     "RLM2"                     "RLM3"                     "RLM4"                    
[73] "RLM5"                     "RLM6"                     "RLM7"                     "RLM8"                     "RLM9"                     "RLM10"                    "RLM11"                    "RLM12"                   
[81] "PSEM1"                    "PSEM2"                   
#AM1 out
#ds <- datos.alumnos.df
#ds.test <- data.frame()
#ds.test <- cbind(ds$genero, ds$admision.letras, ds$admision.numeros, ds$promedio.preparatoria, ds$edad.ingreso, ds$evalucion.socioeconomica, ds$nota.conducta, ds$BECA, ds$AM1 )
ds.data.analisis <- datos.alumnos.df
ds.data.analisis$AM1 <- NULL


dataframe.to.cor <- ds.data.analisis

library(corrplot)
source("http://www.sthda.com/upload/rquery_cormat.r")
rquery.cormat(dataframe.to.cor)
$r

$p

$sym
                         AM9 AM3 AM5 VBM6 PDM6 RLM6 CM6 TM6 AM11 AM12 VBM10 PDM10 RLM10 CM10 TM10 AM7 AM8 AM4 CM1 TM1 RLM1 VBM1 PDM1 BECA CM3 TM3 PDM3 VBM3
AM9                      1                                                                                                                                 
AM3                          1                                                                                                                             
AM5                              1                                                                                                                         
VBM6                                 1                                                                                                                     
PDM6                                 1    1                                                                                                                
RLM6                                 B    B    1                                                                                                           
CM6                                  B    *    B    1                                                                                                      
TM6                                  B    *    B    1   1                                                                                                  
AM11                                                        1                                                                                              
AM12                                                             1                                                                                         
VBM10                                                                 1                                                                                    
PDM10                                                                 B     1                                                                              
                         RLM3 CM2 TM2 PDM2 VBM2 RLM2 CM12 TM12 PDM12 VBM12 RLM12 PSEM1 promedio.preparatoria admision.letras admision.numeros edad.ingreso
AM9                                                                                                                                                       
AM3                                                                                                                                                       
AM5                                                                                                                                                       
VBM6                                                                                                                                                      
PDM6                                                                                                                                                      
RLM6                                                                                                                                                      
CM6                                                                                                                                                       
TM6                                                                                                                                                       
AM11                                                                                                                                                      
AM12                                                                                                                                                      
VBM10                                                                                                                                                     
PDM10                                                                                                                                                     
                         nota.conducta genero evalucion.socioeconomica CM7 TM7 RLM7 VBM7 PDM7 CM4 TM4 RLM4 VBM4 PDM4 CM8 TM8 PDM8 VBM8 RLM8 AM6 AM10 CM9 TM9
AM9                                                                                                                                                         
AM3                                                                                                                                                         
AM5                                                                                                                                                         
VBM6                                                                                                                                                        
PDM6                                                                                                                                                        
RLM6                                                                                                                                                        
CM6                                                                                                                                                         
TM6                                                                                                                                                         
AM11                                                                                                                                                        
AM12                                                                                                                                                        
VBM10                                                                                                                                                       
PDM10                                                                                                                                                       
                         PDM9 VBM9 RLM9 CM5 TM5 PDM5 VBM5 RLM5 CM11 TM11 RLM11 VBM11 PDM11 AM2 PSEM2
AM9                                                                                                 
AM3                                                                                                 
AM5                                                                                                 
VBM6                                                                                                
PDM6                                                                                                
RLM6                                                                                                
CM6                                                                                                 
TM6                                                                                                 
AM11                                                                                                
AM12                                                                                                
VBM10                                                                                               
PDM10                                                                                               
 [ reached getOption("max.print") -- omitted 69 rows ]
attr(,"legend")
[1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1
col<- colorRampPalette(c("blue", "white", "red"))(20)
# create device
?jpeg
jpeg('correlacion-alumnos-ad.jpg', width=1920, height=1080)
rquery.cormat(dataframe.to.cor, type="full", col=col)
$r
                             AM9    AM3      AM5    VBM6    PDM6   RLM6     CM6     TM6    AM11     AM12    VBM10   PDM10  RLM10    CM10    TM10     AM7
AM9                       1.0000  0.150  0.17000  0.1700  0.1700  0.190  0.1600  0.1500  0.1500  0.12000  0.09400  0.0900  0.120  0.0770  0.0810  0.1100
AM3                       0.1500  1.000  0.21000  0.1300  0.1300  0.160  0.1300  0.1200  0.1800  0.16000  0.15000  0.1400  0.180  0.1200  0.1300  0.2000
AM5                       0.1700  0.210  1.00000  0.1200  0.1300  0.150  0.1200  0.1100  0.1300  0.18000  0.10000  0.0950  0.120  0.0840  0.0880  0.2000
VBM6                      0.1700  0.130  0.12000  1.0000  1.0000  0.980  0.9600  0.9600  0.1600  0.16000  0.14000  0.1300  0.160  0.1100  0.1100  0.1800
PDM6                      0.1700  0.130  0.13000  1.0000  1.0000  0.970  0.9200  0.9300  0.1600  0.16000  0.14000  0.1400  0.160  0.1100  0.1200  0.1700
RLM6                      0.1900  0.160  0.15000  0.9800  0.9700  1.000  0.9800  0.9700  0.1900  0.18000  0.16000  0.1500  0.180  0.1200  0.1300  0.2100
CM6                       0.1600  0.130  0.12000  0.9600  0.9200  0.980  1.0000  1.0000  0.1600  0.15000  0.13000  0.1300  0.150  0.1100  0.1100  0.1900
TM6                       0.1500  0.120  0.11000  0.9600  0.9300  0.970  1.0000  1.0000  0.1500  0.14000  0.12000  0.1200  0.140  0.0980  0.1000  0.1800
AM11                      0.1500  0.180  0.13000  0.1600  0.1600  0.190  0.1600  0.1500  1.0000  0.23000  0.14000  0.1300  0.160  0.1100  0.1200  0.1400
AM12                      0.1200  0.160  0.18000  0.1600  0.1600  0.180  0.1500  0.1400  0.2300  1.00000  0.16000  0.1600  0.180  0.1400  0.1400  0.1500
VBM10                     0.0940  0.150  0.10000  0.1400  0.1400  0.160  0.1300  0.1200  0.1400  0.16000  1.00000  0.9900  0.980  0.9500  0.9500  0.1800
PDM10                     0.0900  0.140  0.09500  0.1300  0.1400  0.150  0.1300  0.1200  0.1300  0.16000  0.99000  1.0000  0.960  0.9200  0.9200  0.1700
                             AM8      AM4     CM1     TM1     RLM1    VBM1     PDM1     BECA     CM3     TM3    PDM3    VBM3    RLM3     CM2     TM2    PDM2
AM9                       0.0680  0.14000  0.1600  0.1600  0.17000  0.1800  0.18000  0.06500  0.0410  0.0380  0.0280  0.0520  0.0410  0.0490  0.0470  0.0490
AM3                       0.2100  0.19000  0.1500  0.1500  0.17000  0.1700  0.17000 -0.02800  0.0110  0.0100 -0.0730 -0.0520 -0.0370  0.0860  0.0840  0.0800
AM5                       0.1400  0.11000  0.1600  0.1600  0.18000  0.1900  0.18000  0.01200  0.0770  0.0740  0.0820  0.1000  0.0890  0.0730  0.0710  0.0550
VBM6                      0.1300  0.13000  0.2000  0.2000  0.20000  0.2000  0.19000 -0.00790  0.0870  0.0840  0.1000  0.1200  0.1000  0.0990  0.0970  0.1100
PDM6                      0.1300  0.13000  0.2000  0.2000  0.20000  0.2000  0.20000 -0.00570  0.0850  0.0820  0.1000  0.1200  0.1000  0.1000  0.1000  0.1200
RLM6                      0.1400  0.16000  0.2200  0.2200  0.22000  0.2200  0.22000 -0.01500  0.1000  0.1000  0.1100  0.1400  0.1200  0.1200  0.1100  0.1300
CM6                       0.1200  0.14000  0.1900  0.1900  0.19000  0.1900  0.18000 -0.01600  0.0930  0.0910  0.1000  0.1200  0.1100  0.0900  0.0890  0.1000
TM6                       0.1100  0.13000  0.1800  0.1800  0.17000  0.1700  0.17000 -0.01400  0.0860  0.0840  0.0940  0.1100  0.1000  0.0810  0.0800  0.0930
AM11                      0.1600  0.14000  0.1700  0.1700  0.18000  0.1900  0.19000 -0.02800  0.1100  0.1100  0.1100  0.1400  0.1200  0.1300  0.1300  0.1300
AM12                      0.1400  0.13000  0.1500  0.1500  0.17000  0.1800  0.18000 -0.01300  0.1100  0.1000  0.1100  0.1400  0.1200  0.1300  0.1300  0.1100
VBM10                     0.1500  0.11000  0.1700  0.1700  0.17000  0.1800  0.18000 -0.01200  0.0340  0.0320  0.0360  0.0570  0.0430  0.0720  0.0700  0.0540
PDM10                     0.1400  0.10000  0.1700  0.1700  0.18000  0.1800  0.18000 -0.01200  0.0340  0.0320  0.0400  0.0590  0.0450  0.0710  0.0700  0.0500
                            VBM2    RLM2     CM12    TM12    PDM12   VBM12    RLM12   PSEM1 promedio.preparatoria admision.letras admision.numeros
AM9                       0.0730  0.0580  0.11000  0.1200  0.09400  0.1300  0.12000 -0.2500               8.6e-03         0.00340          0.00340
AM3                       0.1100  0.0950  0.06000  0.0650  0.03200  0.0830  0.06400 -0.2700               3.1e-02         0.03400          0.03400
AM5                       0.0860  0.0730  0.11000  0.1100  0.08900  0.1300  0.11000 -0.2700               3.2e-02         0.03500          0.03500
VBM6                      0.1300  0.1200  0.07200  0.0760  0.05600  0.0930  0.07900 -0.2400              -2.3e-02        -0.01900         -0.01900
PDM6                      0.1400  0.1200  0.07700  0.0810  0.05900  0.0970  0.08300 -0.2500              -2.6e-02        -0.02100         -0.02100
RLM6                      0.1500  0.1300  0.08200  0.0870  0.06300  0.1100  0.09000 -0.2900              -1.5e-02        -0.01000         -0.01000
CM6                       0.1200  0.1100  0.06000  0.0630  0.04700  0.0840  0.06800 -0.2400              -1.2e-02        -0.00800         -0.00800
TM6                       0.1100  0.0960  0.05300  0.0570  0.04200  0.0760  0.06100 -0.2100              -1.5e-02        -0.01100         -0.01100
AM11                      0.1700  0.1500  0.07700  0.0810  0.04900  0.0930  0.07900 -0.2800              -2.5e-02        -0.02900         -0.02900
AM12                      0.1500  0.1300  0.08400  0.0840 -0.04700 -0.0260  0.01300 -0.2800              -6.8e-03        -0.00044         -0.00044
VBM10                     0.0780  0.0700  0.03100  0.0330  0.02500  0.0480  0.03700 -0.2300               3.1e-02         0.03500          0.03500
PDM10                     0.0740  0.0670  0.02800  0.0300  0.02300  0.0450  0.03400 -0.2300               3.0e-02         0.03500          0.03500
                         edad.ingreso nota.conducta   genero evalucion.socioeconomica      CM7     TM7    RLM7     VBM7     PDM7      CM4      TM4   RLM4
AM9                          -0.00170      -4.0e-03 -0.01100                 -0.04700  1.1e-01  0.0950  0.1500  0.12000  0.15000  3.9e-02  0.03900  0.082
AM3                           0.02700       2.7e-02  0.04600                  0.03400  1.2e-01  0.1000  0.1600  0.13000  0.15000  9.4e-02  0.09400  0.140
AM5                           0.03200       3.0e-02  0.01700                  0.00051  1.1e-01  0.0950  0.1300  0.11000  0.13000  1.1e-01  0.11000  0.160
VBM6                         -0.02400      -2.2e-02  0.00680                  0.00570  3.4e-02  0.0190  0.0560  0.03100  0.04900  9.6e-02  0.09600  0.120
PDM6                         -0.02700      -2.5e-02  0.00730                  0.00520  4.0e-02  0.0240  0.0630  0.03700  0.05600  9.7e-02  0.09700  0.120
RLM6                         -0.01600      -1.3e-02  0.01500                  0.01200  4.9e-02  0.0320  0.0730  0.04600  0.06500  1.1e-01  0.11000  0.140
CM6                          -0.01400      -9.4e-03  0.00870                  0.00940  2.3e-02  0.0100  0.0420  0.02000  0.03600  9.5e-02  0.09500  0.120
TM6                          -0.01600      -1.2e-02  0.00480                  0.00670  1.4e-02  0.0024  0.0310  0.01200  0.02600  8.9e-02  0.08900  0.110
AM11                         -0.02700      -2.8e-02  0.02400                  0.01300  1.4e-01  0.1200  0.1700  0.14000  0.16000  8.0e-02  0.08000  0.140
AM12                         -0.00710      -9.3e-03  0.03600                  0.01400  1.1e-01  0.0970  0.1400  0.12000  0.14000  1.2e-01  0.12000  0.170
VBM10                         0.03000       3.1e-02 -0.06300                 -0.01100  5.4e-02  0.0430  0.0760  0.05700  0.07300  7.6e-02  0.07600  0.120
PDM10                         0.02900       3.0e-02 -0.06100                 -0.01000  5.0e-02  0.0390  0.0720  0.05300  0.06900  7.1e-02  0.07100  0.120
                            VBM4     PDM4      CM8     TM8    PDM8    VBM8    RLM8     AM6    AM10     CM9     TM9     PDM9    VBM9    RLM9    CM5     TM5
AM9                       0.0590  0.05900  0.10000  0.1100  0.1200  0.1300  0.1200  0.1000  0.1800 -0.0240 -0.0250 -0.13000 -0.1200 -0.0940  0.043  0.0470
AM3                       0.1200  0.12000  0.07600  0.0860  0.1000  0.1100  0.0940  0.1700  0.1600  0.1000  0.1000  0.10000  0.1300  0.1200  0.068  0.0730
AM5                       0.1500  0.15000  0.14000  0.1400  0.1500  0.1600  0.1500  0.1600  0.1400  0.0480  0.0500  0.06200  0.0800  0.0680  0.020  0.0210
VBM6                      0.1000  0.09500  0.13000  0.1400  0.1300  0.1400  0.1300 -0.0580  0.1400  0.0580  0.0600  0.06100  0.0830  0.0730  0.100  0.1100
PDM6                      0.1000  0.09700  0.14000  0.1500  0.1400  0.1500  0.1400 -0.0820  0.1400  0.0560  0.0580  0.05900  0.0820  0.0710  0.110  0.1100
RLM6                      0.1200  0.11000  0.14000  0.1500  0.1400  0.1600  0.1400 -0.0310  0.1600  0.0810  0.0830  0.08700  0.1100  0.1000  0.110  0.1200
CM6                       0.0970  0.09200  0.11000  0.1200  0.1100  0.1200  0.1100  0.0170  0.1400  0.0690  0.0710  0.07400  0.0960  0.0850  0.092  0.0950
TM6                       0.0880  0.08300  0.11000  0.1200  0.1000  0.1100  0.1000  0.0140  0.1300  0.0600  0.0610  0.06300  0.0830  0.0730  0.086  0.0890
AM11                      0.1100  0.12000  0.07500  0.0850  0.0970  0.1000  0.0890  0.1500  0.1200  0.1000  0.1000  0.09800  0.1200  0.1100  0.092  0.0970
AM12                      0.1500  0.16000  0.08800  0.0980  0.1100  0.1100  0.1000  0.0860  0.1700  0.0500  0.0520  0.06600  0.0880  0.0730  0.083  0.0870
VBM10                     0.1200  0.12000 -0.00033  0.0084  0.0340  0.0350  0.0210  0.1400 -0.0660  0.0680  0.0700  0.09900  0.1200  0.0990  0.064  0.0680
PDM10                     0.1100  0.12000 -0.00380  0.0049  0.0310  0.0320  0.0180  0.1300 -0.0840  0.0610  0.0630  0.09400  0.1100  0.0940  0.065  0.0690
                            PDM5    VBM5    RLM5    CM11    TM11   RLM11   VBM11   PDM11     AM2   PSEM2
AM9                       0.0200  0.0510  0.0390  0.0520  0.0550  0.0760  0.0900  0.0740  0.1000  0.0370
AM3                       0.0550  0.0880  0.0720  0.0780  0.0810  0.1100  0.1300  0.1100  0.1800  0.0780
AM5                      -0.1200 -0.0910 -0.0610  0.0470  0.0500  0.0730  0.0900  0.0690  0.1800  0.0680
VBM6                      0.0890  0.1200  0.1100  0.0380  0.0400  0.0620  0.0750  0.0630  0.1500  0.0710
PDM6                      0.0940  0.1200  0.1100  0.0380  0.0400  0.0620  0.0760  0.0640  0.1600  0.0710
RLM6                      0.0980  0.1300  0.1200  0.0550  0.0580  0.0810  0.0960  0.0800  0.1800  0.0860
CM6                       0.0750  0.1000  0.0920  0.0450  0.0470  0.0650  0.0770  0.0640  0.1400  0.0740
TM6                       0.0680  0.0960  0.0850  0.0380  0.0400  0.0570  0.0680  0.0560  0.1300  0.0680
AM11                      0.0940  0.1300  0.1100  0.0590  0.0590  0.0015 -0.0180 -0.0390  0.1700  0.0760
AM12                      0.0890  0.1100  0.0960  0.0610  0.0640  0.0880  0.1100  0.0840  0.1200  0.0760
VBM10                     0.0420  0.0700  0.0600  0.0200  0.0220  0.0260  0.0340  0.0150  0.1300  0.0630
PDM10                     0.0420  0.0690  0.0600  0.0180  0.0210  0.0240  0.0320  0.0120  0.1200  0.0610
 [ reached getOption("max.print") -- omitted 69 rows ]

$p
                             AM9     AM3     AM5    VBM6    PDM6    RLM6     CM6     TM6    AM11    AM12   VBM10   PDM10   RLM10    CM10    TM10     AM7
AM9                      0.0e+00 1.3e-06 1.0e-07 6.5e-08 3.9e-08 1.6e-09 2.5e-07 1.4e-06 2.6e-06 2.1e-04 2.8e-03 4.6e-03 1.6e-04 1.5e-02 1.0e-02 4.5e-04
AM3                      1.3e-06 0.0e+00 1.1e-11 2.6e-05 2.1e-05 2.7e-07 2.0e-05 1.3e-04 1.7e-08 8.2e-07 2.0e-06 5.3e-06 6.8e-09 7.9e-05 3.8e-05 1.2e-10
AM5                      1.0e-07 1.1e-11 0.0e+00 8.6e-05 5.9e-05 3.0e-06 1.4e-04 6.0e-04 2.8e-05 6.6e-09 1.6e-03 2.7e-03 8.4e-05 7.6e-03 5.3e-03 4.8e-10
VBM6                     6.5e-08 2.6e-05 8.6e-05 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 6.7e-07 8.2e-07 1.5e-05 2.0e-05 7.6e-07 5.1e-04 3.3e-04 1.5e-08
PDM6                     3.9e-08 2.1e-05 5.9e-05 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 6.7e-07 4.4e-07 9.6e-06 1.3e-05 4.5e-07 4.1e-04 2.6e-04 2.8e-08
RLM6                     1.6e-09 2.7e-07 3.0e-06 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 3.5e-09 1.1e-08 7.6e-07 1.1e-06 1.6e-08 7.5e-05 4.2e-05 3.9e-11
CM6                      2.5e-07 2.0e-05 1.4e-04 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 3.3e-07 2.7e-06 3.5e-05 4.9e-05 2.3e-06 8.0e-04 5.3e-04 1.7e-09
TM6                      1.4e-06 1.3e-04 6.0e-04 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 2.9e-06 1.8e-05 1.3e-04 1.8e-04 1.3e-05 1.9e-03 1.3e-03 1.8e-08
AM11                     2.6e-06 1.7e-08 2.8e-05 6.7e-07 6.7e-07 3.5e-09 3.3e-07 2.9e-06 0.0e+00 2.4e-13 1.8e-05 3.4e-05 2.6e-07 4.4e-04 2.5e-04 4.6e-06
AM12                     2.1e-04 8.2e-07 6.6e-09 8.2e-07 4.4e-07 1.1e-08 2.7e-06 1.8e-05 2.4e-13 0.0e+00 3.3e-07 5.4e-07 1.1e-08 1.7e-05 9.8e-06 1.6e-06
VBM10                    2.8e-03 2.0e-06 1.6e-03 1.5e-05 9.6e-06 7.6e-07 3.5e-05 1.3e-04 1.8e-05 3.3e-07 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 2.2e-08
PDM10                    4.6e-03 5.3e-06 2.7e-03 2.0e-05 1.3e-05 1.1e-06 4.9e-05 1.8e-04 3.4e-05 5.4e-07 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 4.7e-08
                             AM8     AM4      CM1      TM1     RLM1     VBM1     PDM1    BECA           CM3     TM3          PDM3    VBM3    RLM3     CM2
AM9                      3.2e-02 9.8e-06  1.8e-07  1.7e-07  2.6e-08  1.7e-08  1.4e-08 3.9e-02  2.000000e-01 2.3e-01  3.800000e-01 9.8e-02 1.9e-01 1.2e-01
AM3                      1.3e-11 2.2e-09  4.1e-06  3.2e-06  1.3e-07  4.7e-08  4.1e-08 3.8e-01  7.300000e-01 7.5e-01  2.100000e-02 1.0e-01 2.5e-01 6.6e-03
AM5                      1.7e-05 3.2e-04  3.5e-07  2.2e-07  1.1e-08  3.3e-09  4.1e-09 7.1e-01  1.500000e-02 1.9e-02  9.900000e-03 1.0e-03 5.0e-03 2.1e-02
VBM6                     4.8e-05 3.4e-05  2.1e-10  1.7e-10  2.9e-10  3.4e-10  5.3e-10 8.0e-01  6.200000e-03 8.0e-03  1.500000e-03 1.1e-04 9.3e-04 1.8e-03
PDM6                     3.2e-05 3.8e-05  1.2e-10  9.7e-11  1.3e-10  1.4e-10  2.1e-10 8.6e-01  7.200000e-03 9.3e-03  1.400000e-03 1.1e-04 9.8e-04 1.1e-03
RLM6                     6.7e-06 3.3e-07  1.0e-12  6.9e-13  7.5e-13  6.9e-13  1.3e-12 6.4e-01  1.100000e-03 1.6e-03  4.000000e-04 1.1e-05 1.5e-04 2.5e-04
CM6                      1.4e-04 1.1e-05  9.0e-10  6.8e-10  1.8e-09  2.2e-09  3.8e-09 6.2e-01  3.200000e-03 4.1e-03  1.600000e-03 1.1e-04 7.1e-04 4.4e-03
TM6                      3.5e-04 7.0e-05  9.9e-09  8.0e-09  2.6e-08  3.5e-08  5.7e-08 6.7e-01  6.300000e-03 7.9e-03  2.800000e-03 2.8e-04 1.5e-03 1.1e-02
AM11                     2.3e-07 4.3e-06  1.0e-07  6.7e-08  4.3e-09  1.4e-09  1.8e-09 3.7e-01  3.800000e-04 6.0e-04  8.400000e-04 9.3e-06 1.2e-04 2.6e-05
AM12                     1.2e-05 6.3e-05  1.1e-06  8.8e-07  3.3e-08  1.2e-08  1.0e-08 6.8e-01  7.900000e-04 1.2e-03  6.700000e-04 1.0e-05 1.5e-04 3.8e-05
VBM10                    3.2e-06 6.0e-04  1.5e-07  1.2e-07  3.2e-08  2.1e-08  2.4e-08 7.1e-01  2.800000e-01 3.2e-01  2.500000e-01 7.1e-02 1.8e-01 2.3e-02
PDM10                    8.8e-06 1.0e-03  9.3e-08  7.4e-08  2.5e-08  1.8e-08  2.1e-08 7.0e-01  2.800000e-01 3.2e-01  2.100000e-01 6.2e-02 1.6e-01 2.5e-02
                             TM2    PDM2    VBM2    RLM2     CM12     TM12    PDM12   VBM12   RLM12    PSEM1 promedio.preparatoria admision.letras
AM9                      1.4e-01 1.2e-01 2.1e-02 6.9e-02  3.6e-04  2.3e-04  2.9e-03 1.9e-05 1.4e-04  1.3e-15                 0.790          0.9100
AM3                      8.1e-03 1.2e-02 2.9e-04 2.6e-03  5.9e-02  4.0e-02  3.1e-01 8.7e-03 4.3e-02  7.4e-18                 0.330          0.2900
AM5                      2.5e-02 8.5e-02 6.7e-03 2.1e-02  6.7e-04  4.4e-04  4.8e-03 5.2e-05 3.0e-04  6.5e-18                 0.300          0.2700
VBM6                     2.1e-03 4.0e-04 3.6e-05 2.6e-04  2.3e-02  1.7e-02  7.8e-02 3.2e-03 1.3e-02  5.5e-15                 0.460          0.5600
PDM6                     1.3e-03 2.3e-04 1.8e-05 1.4e-04  1.5e-02  1.1e-02  6.2e-02 2.1e-03 8.7e-03  1.3e-15                 0.410          0.5100
RLM6                     3.0e-04 6.7e-05 2.2e-06 2.7e-05  9.3e-03  6.2e-03  4.5e-02 7.2e-04 4.5e-03  1.8e-20                 0.630          0.7500
CM6                      4.9e-03 1.4e-03 1.6e-04 8.7e-04  5.9e-02  4.5e-02  1.3e-01 8.0e-03 3.1e-02  4.7e-14                 0.700          0.8000
TM6                      1.2e-02 3.3e-03 5.7e-04 2.5e-03  9.3e-02  7.4e-02  1.8e-01 1.6e-02 5.3e-02  9.5e-12                 0.640          0.7300
AM11                     3.5e-05 3.8e-05 1.4e-07 3.8e-06  1.5e-02  1.0e-02  1.2e-01 3.2e-03 1.2e-02  1.2e-19                 0.440          0.3600
AM12                     4.9e-05 3.5e-04 3.2e-06 2.4e-05  8.1e-03  8.2e-03  1.3e-01 4.1e-01 6.8e-01  3.2e-19                 0.830          0.9900
VBM10                    2.6e-02 8.6e-02 1.4e-02 2.7e-02  3.3e-01  3.0e-01  4.2e-01 1.3e-01 2.4e-01  9.4e-14                 0.330          0.2600
PDM10                    2.8e-02 1.1e-01 1.9e-02 3.4e-02  3.8e-01  3.4e-01  4.8e-01 1.6e-01 2.8e-01  2.1e-13                 0.340          0.2800
                         admision.numeros edad.ingreso nota.conducta   genero evalucion.socioeconomica     CM7     TM7    RLM7    VBM7    PDM7     CM4
AM9                                0.9100       0.9600        0.9000  7.3e-01                  1.4e-01 4.5e-04 2.8e-03 2.8e-06 1.2e-04 3.1e-06 2.2e-01
AM3                                0.2900       0.3900        0.4000  1.5e-01                  2.8e-01 1.7e-04 1.6e-03 5.8e-07 6.6e-05 1.1e-06 2.9e-03
AM5                                0.2700       0.3100        0.3400  6.0e-01                  9.9e-01 6.2e-04 2.6e-03 2.8e-05 4.6e-04 5.2e-05 4.3e-04
VBM6                               0.5600       0.4400        0.4900  8.3e-01                  8.6e-01 2.9e-01 5.5e-01 7.9e-02 3.3e-01 1.2e-01 2.3e-03
PDM6                               0.5100       0.4000        0.4300  8.2e-01                  8.7e-01 2.1e-01 4.4e-01 4.7e-02 2.4e-01 7.8e-02 2.2e-03
RLM6                               0.7500       0.6100        0.6700  6.3e-01                  7.0e-01 1.2e-01 3.1e-01 2.1e-02 1.5e-01 3.9e-02 5.5e-04
CM6                                0.8000       0.6700        0.7700  7.8e-01                  7.7e-01 4.7e-01 7.5e-01 1.9e-01 5.2e-01 2.6e-01 2.6e-03
TM6                                0.7300       0.6100        0.7100  8.8e-01                  8.3e-01 6.6e-01 9.4e-01 3.2e-01 7.2e-01 4.2e-01 5.0e-03
AM11                               0.3600       0.3900        0.3700  4.5e-01                  6.9e-01 8.5e-06 9.9e-05 5.6e-08 6.7e-06 1.9e-07 1.1e-02
AM12                               0.9900       0.8200        0.7700  2.6e-01                  6.6e-01 3.9e-04 2.1e-03 5.7e-06 1.6e-04 8.2e-06 8.4e-05
VBM10                              0.2600       0.3400        0.3300  4.8e-02                  7.2e-01 8.7e-02 1.8e-01 1.6e-02 7.1e-02 2.1e-02 1.7e-02
PDM10                              0.2800       0.3500        0.3500  5.5e-02                  7.5e-01 1.1e-01 2.2e-01 2.3e-02 9.5e-02 2.9e-02 2.5e-02
                             TM4    RLM4    VBM4    PDM4     CM8     TM8    PDM8    VBM8    RLM8     AM6    AM10     CM9     TM9    PDM9    VBM9    RLM9
AM9                      2.2e-01 9.7e-03 6.3e-02 6.1e-02 9.6e-04 3.9e-04 1.0e-04 5.4e-05 2.2e-04 1.4e-03 1.2e-08 4.5e-01 4.4e-01 2.0e-05 1.2e-04 2.9e-03
AM3                      3.0e-03 9.9e-06 1.7e-04 1.5e-04 1.6e-02 6.8e-03 1.0e-03 6.4e-04 3.0e-03 9.3e-08 2.6e-07 1.4e-03 1.1e-03 1.3e-03 3.6e-05 1.8e-04
AM5                      4.4e-04 3.1e-07 3.3e-06 1.6e-06 1.8e-05 4.8e-06 9.1e-07 3.2e-07 2.5e-06 5.2e-07 1.1e-05 1.3e-01 1.1e-01 4.9e-02 1.2e-02 3.2e-02
VBM6                     2.4e-03 1.6e-04 1.6e-03 2.6e-03 3.2e-05 7.8e-06 4.2e-05 8.0e-06 3.8e-05 6.6e-02 1.1e-05 6.8e-02 6.0e-02 5.4e-02 8.4e-03 2.2e-02
PDM6                     2.2e-03 1.2e-04 1.3e-03 2.1e-03 1.4e-05 3.3e-06 1.6e-05 2.8e-06 1.5e-05 9.8e-03 1.2e-05 7.7e-02 6.8e-02 6.3e-02 9.7e-03 2.5e-02
RLM6                     5.7e-04 1.1e-05 2.2e-04 3.7e-04 7.3e-06 1.2e-06 4.2e-06 6.2e-07 5.2e-06 3.4e-01 4.0e-07 1.0e-02 8.3e-03 5.8e-03 3.5e-04 1.5e-03
CM6                      2.7e-03 2.1e-04 2.2e-03 3.7e-03 3.1e-04 8.5e-05 4.2e-04 9.7e-05 4.0e-04 6.0e-01 8.2e-06 2.8e-02 2.4e-02 1.9e-02 2.5e-03 7.2e-03
TM6                      5.1e-03 6.7e-04 5.3e-03 8.8e-03 7.3e-04 2.4e-04 1.4e-03 3.5e-04 1.1e-03 6.7e-01 3.3e-05 6.0e-02 5.3e-02 4.6e-02 8.7e-03 2.0e-02
AM11                     1.1e-02 8.0e-06 3.4e-04 2.6e-04 1.7e-02 6.8e-03 2.1e-03 1.1e-03 5.0e-03 1.6e-06 8.0e-05 1.2e-03 9.6e-04 2.0e-03 9.0e-05 2.9e-04
AM12                     8.8e-05 3.3e-08 9.7e-07 6.9e-07 5.5e-03 1.9e-03 6.3e-04 2.9e-04 1.5e-03 6.7e-03 9.7e-08 1.2e-01 1.0e-01 3.6e-02 5.4e-03 2.0e-02
VBM10                    1.7e-02 7.9e-05 2.5e-04 9.9e-05 9.9e-01 7.9e-01 2.8e-01 2.7e-01 5.1e-01 1.8e-05 3.7e-02 3.2e-02 2.8e-02 1.7e-03 2.5e-04 1.7e-03
PDM10                    2.5e-02 1.4e-04 4.3e-04 1.7e-04 9.0e-01 8.8e-01 3.2e-01 3.1e-01 5.8e-01 4.3e-05 7.7e-03 5.2e-02 4.6e-02 2.8e-03 4.8e-04 3.1e-03
                             CM5     TM5    PDM5    VBM5    RLM5    CM11    TM11   RLM11   VBM11   PDM11      AM2    PSEM2
AM9                      1.8e-01 1.4e-01 5.3e-01 1.1e-01 2.2e-01 9.8e-02 8.3e-02 1.6e-02 4.2e-03 2.0e-02  1.3e-03  2.4e-01
AM3                      3.1e-02 2.1e-02 8.1e-02 5.3e-03 2.3e-02 1.4e-02 1.1e-02 5.5e-04 5.4e-05 6.2e-04  1.0e-08  1.3e-02
AM5                      5.2e-01 5.0e-01 1.2e-04 3.9e-03 5.2e-02 1.4e-01 1.1e-01 2.1e-02 4.2e-03 3.0e-02  1.3e-08  3.0e-02
VBM6                     9.0e-04 5.7e-04 4.9e-03 1.6e-04 7.4e-04 2.3e-01 2.0e-01 5.1e-02 1.8e-02 4.6e-02  1.3e-06  2.5e-02
PDM6                     5.3e-04 3.3e-04 2.9e-03 7.4e-05 4.0e-04 2.3e-01 2.1e-01 4.9e-02 1.6e-02 4.2e-02  5.8e-07  2.5e-02
RLM6                     3.2e-04 1.8e-04 2.0e-03 3.2e-05 2.3e-04 8.2e-02 6.9e-02 1.1e-02 2.5e-03 1.1e-02  2.2e-08  6.6e-03
CM6                      3.7e-03 2.5e-03 1.8e-02 1.1e-03 3.7e-03 1.5e-01 1.4e-01 3.9e-02 1.5e-02 4.2e-02  6.5e-06  1.9e-02
TM6                      6.7e-03 4.7e-03 3.0e-02 2.5e-03 7.1e-03 2.3e-01 2.1e-01 7.4e-02 3.3e-02 7.4e-02  4.1e-05  3.2e-02
AM11                     3.5e-03 2.2e-03 3.0e-03 7.2e-05 8.6e-04 6.3e-02 6.2e-02 9.6e-01 5.6e-01 2.2e-01  2.8e-08  1.6e-02
AM12                     8.6e-03 6.1e-03 5.1e-03 3.8e-04 2.4e-03 5.5e-02 4.2e-02 5.2e-03 7.6e-04 8.1e-03  7.9e-05  1.7e-02
VBM10                    4.2e-02 3.2e-02 1.9e-01 2.8e-02 5.8e-02 5.3e-01 4.8e-01 4.1e-01 2.8e-01 6.3e-01  6.6e-05  4.5e-02
PDM10                    3.9e-02 2.9e-02 1.8e-01 2.8e-02 5.6e-02 5.6e-01 5.1e-01 4.6e-01 3.1e-01 7.0e-01  8.2e-05  5.4e-02
 [ reached getOption("max.print") -- omitted 69 rows ]

$sym
                         AM9 AM3 AM5 VBM6 PDM6 RLM6 CM6 TM6 AM11 AM12 VBM10 PDM10 RLM10 CM10 TM10 AM7 AM8 AM4 CM1 TM1 RLM1 VBM1 PDM1 BECA CM3 TM3 PDM3 VBM3
AM9                      1                                                                                                                                 
AM3                          1                                                                                                                             
AM5                              1                                                                                                                         
VBM6                                 1                                                                                                                     
PDM6                                 1    1                                                                                                                
RLM6                                 B    B    1                                                                                                           
CM6                                  B    *    B    1                                                                                                      
TM6                                  B    *    B    1   1                                                                                                  
AM11                                                        1                                                                                              
AM12                                                             1                                                                                         
VBM10                                                                 1                                                                                    
PDM10                                                                 B     1                                                                              
                         RLM3 CM2 TM2 PDM2 VBM2 RLM2 CM12 TM12 PDM12 VBM12 RLM12 PSEM1 promedio.preparatoria admision.letras admision.numeros edad.ingreso
AM9                                                                                                                                                       
AM3                                                                                                                                                       
AM5                                                                                                                                                       
VBM6                                                                                                                                                      
PDM6                                                                                                                                                      
RLM6                                                                                                                                                      
CM6                                                                                                                                                       
TM6                                                                                                                                                       
AM11                                                                                                                                                      
AM12                                                                                                                                                      
VBM10                                                                                                                                                     
PDM10                                                                                                                                                     
                         nota.conducta genero evalucion.socioeconomica CM7 TM7 RLM7 VBM7 PDM7 CM4 TM4 RLM4 VBM4 PDM4 CM8 TM8 PDM8 VBM8 RLM8 AM6 AM10 CM9 TM9
AM9                                                                                                                                                         
AM3                                                                                                                                                         
AM5                                                                                                                                                         
VBM6                                                                                                                                                        
PDM6                                                                                                                                                        
RLM6                                                                                                                                                        
CM6                                                                                                                                                         
TM6                                                                                                                                                         
AM11                                                                                                                                                        
AM12                                                                                                                                                        
VBM10                                                                                                                                                       
PDM10                                                                                                                                                       
                         PDM9 VBM9 RLM9 CM5 TM5 PDM5 VBM5 RLM5 CM11 TM11 RLM11 VBM11 PDM11 AM2 PSEM2
AM9                                                                                                 
AM3                                                                                                 
AM5                                                                                                 
VBM6                                                                                                
PDM6                                                                                                
RLM6                                                                                                
CM6                                                                                                 
TM6                                                                                                 
AM11                                                                                                
AM12                                                                                                
VBM10                                                                                               
PDM10                                                                                               
 [ reached getOption("max.print") -- omitted 69 rows ]
attr(,"legend")
[1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1
dev.off()
quartz_off_screen 
                2 

cormat<-rquery.cormat(dataframe.to.cor, type="full", col=col)

NA
NA
plot.relaciones <- function(x="", y=""){
  alumnos.training$cluster <- clustering.kmeans$cluster
#plot(alumnos.training)
plot(alumnos.training[,c(x, y) ], 
     col = clustering.kmeans$cluster)
}
plot.relaciones("BECA", "evalucion.socioeconomica")

plot.relaciones("PDM1", "PSEM1")

plot.relaciones("VBM1", "PSEM1")

plot(alumnos.training[,c("VBM1", "PSEM1") ], 
     col = 1:3)

---
title: "R Proyecto Final"
output: html_notebook
---
```{r}
#Modificar variable para especificar directorio del Proyecto Final
#local.path <- "/Users/akcasill/Downloads"

user.path <- "/Users/jos/Downloads/"

local.path <- paste(user.path ,"mcc-ad/data",sep = "")
local.path.imgs <- paste(user.path ,"mcc-ad/imgs",sep = "")
```


```{r}
#Dependencies
#install.packages("png")
library(png)
```

#ASISTENCIAS TOTALES
```{r}
setwd(local.path)
# son 9 semestres de 6 materias cada uno. 
# 1.- Asistencias Totales
load("AsistenciasTotales.R")
class(asistencias.totales)
length(asistencias.totales)
class(asistencias.totales[[1]])
dim(asistencias.totales[[1]])
class(asistencias.totales[1])

asistencias.totales[[1]][1:10,1:10]
```
```{r}
#Asistencias
#===================
#Definición Valores
#===================
# 2 El alumno tiene asistnecia completa.
# 1 El alumno tiene retardo.
# 0 El alumno tiene falta.

#Sólo tomar las primeras 12 materias (Columnas)
for(i in 1:length(asistencias.totales)){
  asistencias.totales[[i]] <- asistencias.totales[[i]][,1:12]
}
asistencias.totales[[4]]
```
Tamaño de Lista de Asistencia de Alumnos:
```{r}
length(asistencias.totales)
```
Lista de total Asistencias por Alumno
```{r}
#
#for(i in 1:length(asistencias.totales)){
#  asistencias.totales[[i]] <- asistencias.totales[[i]][,1:12]
#}
asistencias.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(asistencias.totales)){
  for(j in 1:12){
    asistencias.alumnos[i,j] <- sum(asistencias.totales[[i]][,j])/32    
  }
}

asistencias.alumnos[1,]
asistencias.df <- as.data.frame(asistencias.alumnos)

#Asistencia Materias Ejemplo: AM1 = Asistencia Materia 1
colnames(asistencias.df) <- c('AM1','AM2','AM3','AM4','AM5','AM6','AM7','AM8','AM9','AM10','AM11','AM12')
#DATA FRAME DE ASISTENCIAS ALUMNOS
#=================================
#Suma de asistencias por Materia
#=================================
asistencias.df
```
#PERFIL ALUMNOS
```{r}
setwd(local.path)
print("Summary")
load("perfilAlumnos.R")
#head(perfil.alumnos,1)
str(perfil.alumnos)
summary(perfil.alumnos)

#===================
#Definición Valores
#===================
# Genero: 2 Hombre, 1 Mujer.
# admision.letras: Calificación Examen Admisión Español
# admision.numeros: Calificación Examen Admisión Matemáticas
# promedio.preparatoria: Calificación Promedio Preparatoria   
# edad.ingreso: Edad, variable numérica             
# evalucion.socioeconomica: 1 más privilegiado, 4 menos privilagiado
# nota.conducta: Calificación subjetiva. 

```

```{r}
perfil.alumnos$genero <- factor(perfil.alumnos$genero)
perfil.alumnos$evalucion.socioeconomica <-
  factor(perfil.alumnos$evalucion.socioeconomica)

perfil.alumnos$edad.ingreso <- 
  factor(perfil.alumnos$edad.ingreso)
```


#DATAFRAME CALIFICACIONES ALUMNOS
```{r}
setwd(local.path)
print("Summary")
# 3 1000 matrices de 2 x 54, calificación entre 1 y 20
load("ResultadosExamenes.R")
#resultados.examenes.totales

examenes.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(resultados.examenes.totales)){
  for(j in 1:12){
    examenes.alumnos[i,j] <- sum(resultados.examenes.totales[[i]][,j])/2    
  }
}

examenes.alumnos[1,]
cal.alumnos.df <- as.data.frame(examenes.alumnos)
#Calificaciones Materias Ejemplo: CM2 = Calificiación Promedio Materia 2
colnames(cal.alumnos.df) <- c('CM1','CM2','CM3','CM4','CM5','CM6','CM7','CM8','CM9','CM10','CM11','CM12')
#===================
#Definición Valores
#===================
# CM1: Calificación Materia 1 valor Máximo 20
cal.alumnos.df
```

#TRABAJOS POR CLASE
```{r}
setwd(local.path)
print("Summary")
# 4 1000 matrices de 4 x 54, son 4 trabajos por clase, entre 1 y 20
load("ResultadoTrabajos.R")
resultados.trabajos.totales[[2]][,1]

tareas.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(resultados.trabajos.totales)){
  for(j in 1:12){
    tareas.alumnos[i,j] <- sum(resultados.trabajos.totales[[i]][,j])/4    
  }
}

#tareas.alumnos[1,]
tareas.alumnos.df <- as.data.frame(tareas.alumnos)
#Tareas Materias Ejemplo: TM2 = Tareas Promedio Materia 2
colnames(tareas.alumnos.df) <- c('TM1','TM2','TM3','TM4','TM5','TM6','TM7','TM8','TM9','TM10','TM11','TM12')
#===================
#Definición Valores
#===================
# TM1: Calificación Tarea Materia 1 valor Máximo 20
tareas.alumnos.df
```

#VISITAS BIBLIOTECA
```{r}
# 5 Redondear. Uso físico y virtual. vector. 1000 Matrices, número de veces que asistio a la biblioteca por materia
setwd(local.path)
load("UsoBiblioteca.R")
length(uso.biblioteca.totales)
mi.val <- uso.biblioteca.totales[[1]][1,1]
mi.val
mi.val <- as.data.frame(mi.val)
mi.val

visitas.biblio.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    visitas.biblio.alumnos[i,j] <- uso.biblioteca.totales[[i]][1,j]
  }
}

visitas.biblio.alumnos.df <- as.data.frame(visitas.biblio.alumnos)
#Visitas Biblioteca  Ejemplo: VBM2 = Visitas Biblioteca  Materia 2
colnames(visitas.biblio.alumnos.df) <- c('VBM1','VBM2','VBM3','VBM4','VBM5','VBM6','VBM7','VBM8','VBM9','VBM10','VBM11','VBM12')
#===================
#Definición Valores
#===================
# VBM1: Visitas Biblioteca Materia 1
visitas.biblio.alumnos.df


```

#USO DE PLATAFORMAS DIGITALES
```{r}
# 6 Redondear, vector. Uso de Canvas o de Plataforma digital.
setwd(local.path)
load("UsoPlataforma.R")
#uso.plataforma.totales
uso.plataforma.totales[[1]][,1:12]
plataformas.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    plataformas.alumnos[i,j] <- uso.plataforma.totales[[i]][1,j]
  }
}

#tareas.alumnos[1,]
plataformas.alumnos.df <- as.data.frame(plataformas.alumnos)
#Tareas Materias Ejemplo: TM2 = Tareas Promedio Materia 2
colnames(plataformas.alumnos.df) <- c('PDM1','PDM2','PDM3','PDM4','PDM5','PDM6','PDM7','PDM8','PDM9','PDM10','PDM11','PDM12')
#===================
#Definición Valores
#===================
# PDM1: Plataformas Digitales Materia 1 valor Máximo 20
plataformas.alumnos.df

```

#APARTADO DE LIBROS POR MATERIA
```{r}
# 7
setwd(local.path)
load("ApartadoDeLibros.R") #1000 matrices, cantidad de libros que el alumno reservó por materia.
separacion.libros.totales[[1]][,1:12]

reserva.libros.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    reserva.libros.alumnos[i,j] <- separacion.libros.totales[[i]][1,j]
  }
}

reserva.libros.alumnos.df <- as.data.frame(reserva.libros.alumnos)
#Reserva de Libris Ejemplo: RLM2 = Reserva de Libros Por Materia 2
colnames(reserva.libros.alumnos.df) <- c('RLM1','RLM2','RLM3','RLM4','RLM5','RLM6','RLM7','RLM8','RLM9','RLM10','RLM11','RLM12')
#===================
#Definición Valores
#===================
# RLM1: Reserva de Libros pro Materia 1
reserva.libros.alumnos.df

```

#DISTRIBUCIÓN DE BECAS ALUMNOS
```{r}
# 8 vector binario, 1 tiene beca, 0 no tiene Beca
setwd(local.path)
load("Becas.R")
distribucion.becas[[1]]
sum(distribucion.becas)

becas.alumnos <- matrix(1:1000, nrow=1000, ncol=1)

for(i in 1:1000){
    becas.alumnos[i] <- distribucion.becas[i]
}

becas.alumnos.df <- as.data.frame(becas.alumnos)
colnames(becas.alumnos.df) <- c('BECA')
#===================
#Definición Valores
#===================
# BECA: Tiene Beca 1
becas.alumnos.df
#Necesita ser un factor por que solo tiene dos valores 0 o 1 
becas.alumnos.df$BECA <- as.factor(becas.alumnos.df$BECA)
becas.alumnos.df
```

#HISTORIAL DE PAGOS ALUMNOS
```{r}
# 9  2 en tiempo, 1 retraso, 0, Son 9 semestres pero hay que user sólo 2 primeras columnas, 4 pagos.
setwd(local.path)
load("HistorialPagos.R")
length(registro.pagos)
registro.pagos[[500]]


pagos.alumnos <- matrix(1:2000, nrow=1000, ncol=2)

for(i in 1:1000){
  for(j in 1:2){
    pagos.alumnos[i,j] <- sum(registro.pagos[[i]][,j])/4    
  }
}

#tareas.alumnos[1,]
pagos.alumnos.df <- as.data.frame(pagos.alumnos)
#Pago Semestre: PSEM2 = Pago Semestre 2
colnames(pagos.alumnos.df) <- c('PSEM1','PSEM2')
#===================
#Definición Valores
#===================
# PSEM1: Suma de pagos semestre 1, 2 valor máximo.
pagos.alumnos.df

```



```{r}
datos.alumnos.df <- cbind.data.frame(perfil.alumnos,
                                     becas.alumnos.df,
                              asistencias.df,
                              cal.alumnos.df,
                              tareas.alumnos.df,
                              visitas.biblio.alumnos.df,
                              plataformas.alumnos.df,
                              reserva.libros.alumnos.df,
                              pagos.alumnos.df)
datos.alumnos.df

```

```{r}
str(datos.alumnos.df)
```

```{r}
summary(datos.alumnos.df)
datos.integrados <- datos.alumnos.df
```

```{r}
setwd(local.path)
save(datos.integrados, file="datos.integrados.R")
getwd()
load("datos.integrados.R")
datos.integrados
head(datos.integrados)

```

#Separar 100 alumnos que no entraran en Kmeans
```{r}
set.seed(1234)

ind <- sample(x=c(0,1),size=nrow(datos.integrados),
              replace=TRUE,prob = c(0.9,0.1))
ind

alumnos.nuevos <- datos.integrados[ind==1,]
alumnos.actuales <- datos.integrados[ind==0,]

alumnos.nuevos
alumnos.actuales
```


```{r}
summary(alumnos.nuevos)
```

```{r}
summary(alumnos.actuales)
```


```{r}
set.seed(1234)

ind <- sample(x=c(0,1),size=nrow(alumnos.actuales),
              replace=TRUE,prob = c(0.7,0.3))
ind

alumnos.training <- alumnos.actuales[ind==0,]
alumnos.test <- alumnos.actuales[ind==1,]

str(alumnos.training)
```

```{r}
set.seed(2020)
wss.alumnos <-vector()
wss.alumnos
centroides.alumnos <- 25
for ( i in 1:centroides.alumnos ) wss.alumnos[i] <- sum(kmeans(alumnos.training,centers = i)$withinss)

#plot
plot(1:centroides.alumnos  , wss.alumnos , type="b", xlab="Numer de clusters", ylab="Error standard")
```



### Analísis de hombro, brazo, codo para seleccionar el centroide
```{r}
imgPath.codo <- paste(local.path.imgs,"/Kmeans-codo-alumnos.png",sep = "")


img.codo.alumnos <- readPNG(imgPath.codo)
plot.new()
rasterImage(img.codo.alumnos,0,0,1,1)
```

```{r}
set.seed(2020)
wss.alumnos <-vector()
wss.alumnos
centroides.alumnos <- 10
for ( i in 1:centroides.alumnos ) wss.alumnos[i] <- sum(kmeans(alumnos.training,centers = i)$withinss)

#plot
plot(1:centroides.alumnos  , wss.alumnos , type="b", xlab="Numer de clusters", ylab="Error standard")
```

```{r}
imgPath.codo.seleccionado <- paste(local.path.imgs,"/Kmeans-codo-alumnos-seleccionado.png",sep = "")


img.codo.sel.alumnos <- readPNG(imgPath.codo.seleccionado)
plot.new()
rasterImage(img.codo.sel.alumnos,0,0,1,1)

```


```{r}

centroides.alumnos <- 4
clustering.kmeans <- kmeans(x=alumnos.training, centers = centroides.alumnos)
clustering.kmeans$withinss
```

```{r}
alumnos.training$genero <- as.numeric(alumnos.training$genero)
alumnos.training$edad.ingreso <- as.numeric(alumnos.training$edad.ingreso)
alumnos.training$evalucion.socioeconomica <- as.numeric(alumnos.training$evalucion.socioeconomica)
alumnos.training$BECA <- as.numeric(alumnos.training$BECA)
sum(is.na(alumnos.training))
#str(alumnos.training)
```

```{r}
datos.alumnos.df$genero <- as.numeric(datos.alumnos.df$genero)
datos.alumnos.df$edad.ingreso <- as.numeric(datos.alumnos.df$edad.ingreso)
datos.alumnos.df$BECA <- as.numeric(datos.alumnos.df$BECA)
datos.alumnos.df$evalucion.socioeconomica <- as.numeric(datos.alumnos.df$evalucion.socioeconomica)
str(datos.alumnos.df)

#ds.test <- data.frame()
#ds.test <- cbind(datos.alumnos.df$genero, datos.alumnos.df$CM1)
#str(ds.test)
```

```{r}
sum(datos.alumnos.df$AM11)
colnames(datos.alumnos.df)
#AM1 out
```



```{r}
#ds <- datos.alumnos.df
#ds.test <- data.frame()
#ds.test <- cbind(ds$genero, ds$admision.letras, ds$admision.numeros, ds$promedio.preparatoria, ds$edad.ingreso, ds$evalucion.socioeconomica, ds$nota.conducta, ds$BECA, ds$AM1 )
ds.data.analisis <- datos.alumnos.df
ds.data.analisis$AM1 <- NULL


dataframe.to.cor <- ds.data.analisis

library(corrplot)
source("http://www.sthda.com/upload/rquery_cormat.r")
rquery.cormat(dataframe.to.cor)

col<- colorRampPalette(c("blue", "white", "red"))(20)
# create device
?jpeg
jpeg('correlacion-alumnos-ad.jpg', width=1920, height=1080)
rquery.cormat(dataframe.to.cor, type="full", col=col)
dev.off()
cormat<-rquery.cormat(dataframe.to.cor, type="full", col=col)


```


```{r}
plot.relaciones <- function(x="", y=""){
  alumnos.training$cluster <- clustering.kmeans$cluster
#plot(alumnos.training)
plot(alumnos.training[,c(x, y) ], 
     col = clustering.kmeans$cluster)
}
```

```{r}
plot.relaciones("BECA", "evalucion.socioeconomica")
```

```{r}
plot.relaciones("PDM1", "PSEM1")
```

```{r}
plot.relaciones("VBM1", "PSEM1")
```


```{r}
plot(alumnos.training[,c("VBM1", "PSEM1") ], 
     col = 1:3)
```



















